Overview

Dataset statistics

Number of variables90
Number of observations5359
Missing cells37661
Missing cells (%)7.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 MiB
Average record size in memory587.0 B

Variable types

Categorical28
Numeric8
Boolean53
Unsupported1

Alerts

IsFtlcApproved has constant value ""Constant
IsBosApproved has constant value ""Constant
CriticalThinking has constant value ""Constant
DataSynthesis has constant value ""Constant
ActiveLearning has constant value ""Constant
Numeracy has constant value ""Constant
Literacy has constant value ""Constant
SelfAwarenessAndReflection has constant value ""Constant
InnovationAndCreativity has constant value ""Constant
Initiative has constant value ""Constant
Independence has constant value ""Constant
Adaptability has constant value ""Constant
ProblemSolving has constant value ""Constant
Budgeting has constant value ""Constant
Oral has constant value ""Constant
ForeignLanguages has constant value ""Constant
Interpersonal has constant value ""Constant
WrittenOther has constant value ""Constant
Collaboration has constant value ""Constant
RelationshipBuilding has constant value ""Constant
Leadership has constant value ""Constant
Negotiation has constant value ""Constant
PeerAssessmentReview has constant value ""Constant
OccupationalAwareness has constant value ""Constant
MarketAwareness has constant value ""Constant
GovernanceAwareness has constant value ""Constant
FinancialAwareness has constant value ""Constant
BusinessPlanning has constant value ""Constant
EthicalAwareness has constant value ""Constant
SocialCulturalGlobalAwareness has constant value ""Constant
LegalAwareness has constant value ""Constant
SourceMaterials has constant value ""Constant
SynthesiseAndPresentMaterials has constant value ""Constant
UseOfComputerApplications has constant value ""Constant
GoalSettingAndActionPlanning has constant value ""Constant
DecisionMaking has constant value ""Constant
AcademicYear has constant value ""Constant
AcademicYearId has constant value ""Constant
IsThemedAgeing has constant value ""Constant
IsThemedSocialRenewal has constant value ""Constant
IsThemedSustainability has constant value ""Constant
ModuleCode has a high cardinality: 5359 distinct valuesHigh cardinality
Title has a high cardinality: 4522 distinct valuesHigh cardinality
ShortTitle has a high cardinality: 4391 distinct valuesHigh cardinality
DateFtlcApproved has a high cardinality: 194 distinct valuesHigh cardinality
DateBosApproved has a high cardinality: 261 distinct valuesHigh cardinality
DateSapUploaded has a high cardinality: 505 distinct valuesHigh cardinality
PreRequisiteComment has a high cardinality: 1008 distinct valuesHigh cardinality
CoRequisiteComment has a high cardinality: 254 distinct valuesHigh cardinality
Aims has a high cardinality: 4570 distinct valuesHigh cardinality
OutlineOfSyllabus has a high cardinality: 4498 distinct valuesHigh cardinality
IntendedKnowledgeOutcomes has a high cardinality: 4371 distinct valuesHigh cardinality
IntendedSkillOutcomes has a high cardinality: 4346 distinct valuesHigh cardinality
TeachingRationaleAndRelationship has a high cardinality: 4098 distinct valuesHigh cardinality
AssessmentRationaleAndRelationship has a high cardinality: 4286 distinct valuesHigh cardinality
ExemptFromAssessmentDate has a high cardinality: 67 distinct valuesHigh cardinality
ExemptFromAssessmentComment has a high cardinality: 226 distinct valuesHigh cardinality
GeneralNotes has a high cardinality: 289 distinct valuesHigh cardinality
Timestamp has a high cardinality: 68 distinct valuesHigh cardinality
SapObjectId is highly overall correlated with Module_Id and 2 other fieldsHigh correlation
Semester1CreditValue is highly overall correlated with Semester1Offered and 1 other fieldsHigh correlation
Semester2CreditValue is highly overall correlated with Semester1Offered and 2 other fieldsHigh correlation
Semester3CreditValue is highly overall correlated with Semester3Offered and 1 other fieldsHigh correlation
EctsCreditValue is highly overall correlated with Semester3OfferedHigh correlation
Module_Id is highly overall correlated with SapObjectId and 4 other fieldsHigh correlation
IsNew is highly overall correlated with Module_Id and 3 other fieldsHigh correlation
IsDummy is highly overall correlated with ExemptFromAssessmentDate and 1 other fieldsHigh correlation
Semester1Offered is highly overall correlated with Semester1CreditValue and 1 other fieldsHigh correlation
Semester2Offered is highly overall correlated with Semester1CreditValue and 1 other fieldsHigh correlation
Semester3Offered is highly overall correlated with Semester3CreditValue and 1 other fieldsHigh correlation
Mode is highly overall correlated with ExemptFromAssessmentDate and 1 other fieldsHigh correlation
Delivery is highly overall correlated with ExemptFromAssessmentDateHigh correlation
StandAloneAvailability is highly overall correlated with ExemptFromAssessmentDateHigh correlation
IsUploadedToSap is highly overall correlated with SapObjectId and 1 other fieldsHigh correlation
GraduateSkillsFrameworkApplicable is highly overall correlated with Module_Id and 3 other fieldsHigh correlation
ExemptFromAssessment is highly overall correlated with ExemptFromAssessmentDate and 1 other fieldsHigh correlation
ExemptFromAssessmentDate is highly overall correlated with SapObjectId and 15 other fieldsHigh correlation
IsHepatitisAImmunisationOffered is highly overall correlated with IsHepatitisBImmunisationOffered and 3 other fieldsHigh correlation
IsHepatitisBImmunisationOffered is highly overall correlated with IsHepatitisAImmunisationOffered and 3 other fieldsHigh correlation
IsTetanusImmunisationOffered is highly overall correlated with ExemptFromAssessmentDate and 3 other fieldsHigh correlation
SchoolCode is highly overall correlated with IsDummy and 8 other fieldsHigh correlation
Timestamp is highly overall correlated with Module_Id and 6 other fieldsHigh correlation
IsSapUploadDisabled is highly overall correlated with ExemptFromAssessmentDate and 2 other fieldsHigh correlation
TeachingLocation is highly overall correlated with ExemptFromAssessmentDate and 2 other fieldsHigh correlation
IsNew is highly imbalanced (71.7%)Imbalance
IsDummy is highly imbalanced (92.6%)Imbalance
Semester3Offered is highly imbalanced (68.4%)Imbalance
Delivery is highly imbalanced (76.8%)Imbalance
StandAloneAvailability is highly imbalanced (71.2%)Imbalance
IsUploadedToSap is highly imbalanced (73.6%)Imbalance
CoRequisiteComment is highly imbalanced (54.5%)Imbalance
Availability is highly imbalanced (90.5%)Imbalance
GraduateSkillsFrameworkApplicable is highly imbalanced (75.5%)Imbalance
ExemptFromAssessment is highly imbalanced (56.7%)Imbalance
IsHepatitisAImmunisationOffered is highly imbalanced (93.4%)Imbalance
IsHepatitisBImmunisationOffered is highly imbalanced (92.0%)Imbalance
IsTetanusImmunisationOffered is highly imbalanced (88.9%)Imbalance
IsAllergyScreeningOffered is highly imbalanced (97.2%)Imbalance
GeneralNotes is highly imbalanced (55.6%)Imbalance
MarkingScale is highly imbalanced (54.9%)Imbalance
Timestamp is highly imbalanced (87.1%)Imbalance
IsSapUploadDisabled is highly imbalanced (91.7%)Imbalance
TeachingLocation is highly imbalanced (73.4%)Imbalance
DateFtlcApproved has 654 (12.2%) missing valuesMissing
DateBosApproved has 478 (8.9%) missing valuesMissing
DateSapUploaded has 123 (2.3%) missing valuesMissing
PreRequisiteComment has 3519 (65.7%) missing valuesMissing
CoRequisiteComment has 4255 (79.4%) missing valuesMissing
Aims has 115 (2.1%) missing valuesMissing
OutlineOfSyllabus has 345 (6.4%) missing valuesMissing
IntendedKnowledgeOutcomes has 286 (5.3%) missing valuesMissing
IntendedSkillOutcomes has 314 (5.9%) missing valuesMissing
CriticalThinking has 217 (4.0%) missing valuesMissing
DataSynthesis has 217 (4.0%) missing valuesMissing
ActiveLearning has 217 (4.0%) missing valuesMissing
Numeracy has 217 (4.0%) missing valuesMissing
Literacy has 217 (4.0%) missing valuesMissing
SelfAwarenessAndReflection has 217 (4.0%) missing valuesMissing
InnovationAndCreativity has 217 (4.0%) missing valuesMissing
Initiative has 217 (4.0%) missing valuesMissing
Independence has 217 (4.0%) missing valuesMissing
Adaptability has 217 (4.0%) missing valuesMissing
ProblemSolving has 217 (4.0%) missing valuesMissing
Budgeting has 217 (4.0%) missing valuesMissing
Oral has 217 (4.0%) missing valuesMissing
ForeignLanguages has 217 (4.0%) missing valuesMissing
Interpersonal has 217 (4.0%) missing valuesMissing
WrittenOther has 217 (4.0%) missing valuesMissing
Collaboration has 217 (4.0%) missing valuesMissing
RelationshipBuilding has 217 (4.0%) missing valuesMissing
Leadership has 217 (4.0%) missing valuesMissing
Negotiation has 217 (4.0%) missing valuesMissing
PeerAssessmentReview has 217 (4.0%) missing valuesMissing
OccupationalAwareness has 217 (4.0%) missing valuesMissing
MarketAwareness has 217 (4.0%) missing valuesMissing
GovernanceAwareness has 217 (4.0%) missing valuesMissing
FinancialAwareness has 217 (4.0%) missing valuesMissing
BusinessPlanning has 217 (4.0%) missing valuesMissing
EthicalAwareness has 217 (4.0%) missing valuesMissing
SocialCulturalGlobalAwareness has 217 (4.0%) missing valuesMissing
LegalAwareness has 217 (4.0%) missing valuesMissing
SourceMaterials has 217 (4.0%) missing valuesMissing
SynthesiseAndPresentMaterials has 217 (4.0%) missing valuesMissing
UseOfComputerApplications has 217 (4.0%) missing valuesMissing
GoalSettingAndActionPlanning has 217 (4.0%) missing valuesMissing
DecisionMaking has 217 (4.0%) missing valuesMissing
TeachingRationaleAndRelationship has 339 (6.3%) missing valuesMissing
AssessmentRationaleAndRelationship has 224 (4.2%) missing valuesMissing
ExemptFromAssessmentDate has 4879 (91.0%) missing valuesMissing
ExemptFromAssessmentComment has 5039 (94.0%) missing valuesMissing
GeneralNotes has 4339 (81.0%) missing valuesMissing
NonStandardSessionOfOffering_id has 5359 (100.0%) missing valuesMissing
ModuleCode is uniformly distributedUniform
Title is uniformly distributedUniform
ShortTitle is uniformly distributedUniform
OutlineOfSyllabus is uniformly distributedUniform
IntendedSkillOutcomes is uniformly distributedUniform
ModuleCode has unique valuesUnique
Module_Id has unique valuesUnique
NonStandardSessionOfOffering_id is an unsupported type, check if it needs cleaning or further analysisUnsupported
SapObjectId has 81 (1.5%) zerosZeros
Semester1CreditValue has 2238 (41.8%) zerosZeros
Semester2CreditValue has 2145 (40.0%) zerosZeros
Semester3CreditValue has 5065 (94.5%) zerosZeros
EctsCreditValue has 142 (2.6%) zerosZeros

Reproduction

Analysis started2023-05-11 20:36:00.232683
Analysis finished2023-05-11 20:36:22.693364
Duration22.46 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

ModuleCode
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct5359
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
ACC2002
 
1
ENG3101
 
1
MAS3998
 
1
CSC3999
 
1
CSC3998
 
1
Other values (5354)
5354 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters37513
Distinct characters35
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5359 ?
Unique (%)100.0%

Sample

1st rowACC2002
2nd rowACC2003
3rd rowACC2005
4th rowACC2007
5th rowACC2008

Common Values

ValueCountFrequency (%)
ACC2002 1
 
< 0.1%
ENG3101 1
 
< 0.1%
MAS3998 1
 
< 0.1%
CSC3999 1
 
< 0.1%
CSC3998 1
 
< 0.1%
ARC8120 1
 
< 0.1%
ARC8119 1
 
< 0.1%
ARC8118 1
 
< 0.1%
ARC8117 1
 
< 0.1%
ARC8116 1
 
< 0.1%
Other values (5349) 5349
99.8%

Length

2023-05-11T21:36:22.765431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
acc2002 1
 
< 0.1%
acc3020 1
 
< 0.1%
acc2007 1
 
< 0.1%
acc2008 1
 
< 0.1%
acc2009 1
 
< 0.1%
acc2020 1
 
< 0.1%
acc2021 1
 
< 0.1%
acc2024 1
 
< 0.1%
acc2025 1
 
< 0.1%
acc2055 1
 
< 0.1%
Other values (5349) 5349
99.8%

Most occurring characters

ValueCountFrequency (%)
0 5265
14.0%
1 3130
 
8.3%
8 2932
 
7.8%
2 2725
 
7.3%
3 2543
 
6.8%
S 2185
 
5.8%
C 2032
 
5.4%
E 1808
 
4.8%
M 1329
 
3.5%
4 1126
 
3.0%
Other values (25) 12438
33.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21436
57.1%
Uppercase Letter 16077
42.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 2185
13.6%
C 2032
12.6%
E 1808
11.2%
M 1329
 
8.3%
A 1085
 
6.7%
L 830
 
5.2%
P 797
 
5.0%
H 770
 
4.8%
N 751
 
4.7%
U 705
 
4.4%
Other values (15) 3785
23.5%
Decimal Number
ValueCountFrequency (%)
0 5265
24.6%
1 3130
14.6%
8 2932
13.7%
2 2725
12.7%
3 2543
11.9%
4 1126
 
5.3%
5 1111
 
5.2%
9 976
 
4.6%
6 869
 
4.1%
7 759
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21436
57.1%
Latin 16077
42.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 2185
13.6%
C 2032
12.6%
E 1808
11.2%
M 1329
 
8.3%
A 1085
 
6.7%
L 830
 
5.2%
P 797
 
5.0%
H 770
 
4.8%
N 751
 
4.7%
U 705
 
4.4%
Other values (15) 3785
23.5%
Common
ValueCountFrequency (%)
0 5265
24.6%
1 3130
14.6%
8 2932
13.7%
2 2725
12.7%
3 2543
11.9%
4 1126
 
5.3%
5 1111
 
5.2%
9 976
 
4.6%
6 869
 
4.1%
7 759
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5265
14.0%
1 3130
 
8.3%
8 2932
 
7.8%
2 2725
 
7.3%
3 2543
 
6.8%
S 2185
 
5.8%
C 2032
 
5.4%
E 1808
 
4.8%
M 1329
 
3.5%
4 1126
 
3.0%
Other values (25) 12438
33.2%

SapObjectId
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5279
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51097933
Minimum0
Maximum53517920
Zeros81
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size42.0 KiB
2023-05-11T21:36:22.875531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50343396
Q151047078
median51963486
Q352730446
95-th percentile53173579
Maximum53517920
Range53517920
Interquartile range (IQR)1683367

Descriptive statistics

Standard deviation6397022.6
Coefficient of variation (CV)0.12519142
Kurtosis58.613604
Mean51097933
Median Absolute Deviation (MAD)778192
Skewness-7.6989159
Sum2.7383383 × 1011
Variance4.0921898 × 1013
MonotonicityNot monotonic
2023-05-11T21:36:23.003656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
1.5%
52534393 1
 
< 0.1%
52534391 1
 
< 0.1%
52455237 1
 
< 0.1%
52351826 1
 
< 0.1%
52352380 1
 
< 0.1%
52354777 1
 
< 0.1%
52354778 1
 
< 0.1%
52353591 1
 
< 0.1%
52353592 1
 
< 0.1%
Other values (5269) 5269
98.3%
ValueCountFrequency (%)
0 81
1.5%
50340718 1
 
< 0.1%
50340719 1
 
< 0.1%
50340720 1
 
< 0.1%
50340721 1
 
< 0.1%
50340733 1
 
< 0.1%
50340739 1
 
< 0.1%
50340740 1
 
< 0.1%
50340741 1
 
< 0.1%
50340743 1
 
< 0.1%
ValueCountFrequency (%)
53517920 1
< 0.1%
53517919 1
< 0.1%
53517918 1
< 0.1%
53517917 1
< 0.1%
53517916 1
< 0.1%
53517915 1
< 0.1%
53456274 1
< 0.1%
53456273 1
< 0.1%
53432699 1
< 0.1%
53432698 1
< 0.1%

Title
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct4522
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
Academic Tutoring
 
33
Dissertation
 
28
Research Project
 
10
Research Methods
 
7
Intercalating Year Reflective Learning Account
 
7
Other values (4517)
5274 

Length

Max length151
Median length94
Mean length35.743609
Min length3

Characters and Unicode

Total characters191550
Distinct characters86
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3906 ?
Unique (%)72.9%

Sample

1st rowManagerial and Business Economics
2nd rowFinancial Control
3rd rowIntermediate Financial Accounting
4th rowResponsible Corporate Finance
5th rowIntroduction to Corporate Finance

Common Values

ValueCountFrequency (%)
Academic Tutoring 33
 
0.6%
Dissertation 28
 
0.5%
Research Project 10
 
0.2%
Research Methods 7
 
0.1%
Intercalating Year Reflective Learning Account 7
 
0.1%
Intercalating Year Personal Learning Record 7
 
0.1%
International Entrepreneurship 6
 
0.1%
English for Academic Purposes (40 Credits Version) 6
 
0.1%
Financial Analysis 5
 
0.1%
Consumer Behaviour 5
 
0.1%
Other values (4512) 5245
97.9%

Length

2023-05-11T21:36:23.143783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 1789
 
7.0%
in 663
 
2.6%
of 510
 
2.0%
the 504
 
2.0%
for 385
 
1.5%
362
 
1.4%
project 310
 
1.2%
research 298
 
1.2%
to 277
 
1.1%
1 264
 
1.0%
Other values (2978) 20018
78.9%

Most occurring characters

ValueCountFrequency (%)
20041
 
10.5%
e 16588
 
8.7%
n 14591
 
7.6%
i 14020
 
7.3%
a 13221
 
6.9%
t 11883
 
6.2%
o 11002
 
5.7%
r 10457
 
5.5%
s 9470
 
4.9%
c 7121
 
3.7%
Other values (76) 63156
33.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 146865
76.7%
Uppercase Letter 20403
 
10.7%
Space Separator 20041
 
10.5%
Other Punctuation 1538
 
0.8%
Decimal Number 1320
 
0.7%
Open Punctuation 493
 
0.3%
Close Punctuation 492
 
0.3%
Dash Punctuation 393
 
0.2%
Final Punctuation 3
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 16588
11.3%
n 14591
9.9%
i 14020
9.5%
a 13221
9.0%
t 11883
 
8.1%
o 11002
 
7.5%
r 10457
 
7.1%
s 9470
 
6.4%
c 7121
 
4.8%
l 6555
 
4.5%
Other values (22) 31957
21.8%
Uppercase Letter
ValueCountFrequency (%)
S 2295
11.2%
P 2129
10.4%
M 1675
 
8.2%
A 1665
 
8.2%
C 1629
 
8.0%
E 1498
 
7.3%
I 1485
 
7.3%
D 1179
 
5.8%
T 993
 
4.9%
L 914
 
4.5%
Other values (16) 4941
24.2%
Decimal Number
ValueCountFrequency (%)
1 449
34.0%
2 340
25.8%
0 146
 
11.1%
3 78
 
5.9%
9 65
 
4.9%
4 63
 
4.8%
5 50
 
3.8%
8 47
 
3.6%
6 45
 
3.4%
7 37
 
2.8%
Other Punctuation
ValueCountFrequency (%)
: 693
45.1%
, 470
30.6%
& 216
 
14.0%
. 58
 
3.8%
/ 46
 
3.0%
' 29
 
1.9%
; 15
 
1.0%
? 11
 
0.7%
Open Punctuation
ValueCountFrequency (%)
( 479
97.2%
[ 14
 
2.8%
Close Punctuation
ValueCountFrequency (%)
) 478
97.2%
] 14
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 385
98.0%
8
 
2.0%
Space Separator
ValueCountFrequency (%)
20041
100.0%
Final Punctuation
ValueCountFrequency (%)
3
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 167268
87.3%
Common 24282
 
12.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 16588
 
9.9%
n 14591
 
8.7%
i 14020
 
8.4%
a 13221
 
7.9%
t 11883
 
7.1%
o 11002
 
6.6%
r 10457
 
6.3%
s 9470
 
5.7%
c 7121
 
4.3%
l 6555
 
3.9%
Other values (48) 52360
31.3%
Common
ValueCountFrequency (%)
20041
82.5%
: 693
 
2.9%
( 479
 
2.0%
) 478
 
2.0%
, 470
 
1.9%
1 449
 
1.8%
- 385
 
1.6%
2 340
 
1.4%
& 216
 
0.9%
0 146
 
0.6%
Other values (18) 585
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 191523
> 99.9%
None 15
 
< 0.1%
Punctuation 12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20041
 
10.5%
e 16588
 
8.7%
n 14591
 
7.6%
i 14020
 
7.3%
a 13221
 
6.9%
t 11883
 
6.2%
o 11002
 
5.7%
r 10457
 
5.5%
s 9470
 
4.9%
c 7121
 
3.7%
Other values (67) 63129
33.0%
Punctuation
ValueCountFrequency (%)
8
66.7%
3
 
25.0%
1
 
8.3%
None
ValueCountFrequency (%)
ó 4
26.7%
à 3
20.0%
é 3
20.0%
ñ 2
13.3%
í 2
13.3%
è 1
 
6.7%

ShortTitle
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct4391
Distinct (%)81.9%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
Dissertation
 
39
Academic Tutoring
 
34
TBC
 
31
Research Methods
 
12
Research Project
 
12
Other values (4386)
5231 

Length

Max length40
Median length28
Mean length25.86434
Min length3

Characters and Unicode

Total characters138607
Distinct characters85
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3740 ?
Unique (%)69.8%

Sample

1st rowManagerial and Business Economics
2nd rowFinancial Control
3rd rowIntermediate Financial Accounting
4th rowResponsible Corporate Finance
5th rowIntroduction to Corporate Finance

Common Values

ValueCountFrequency (%)
Dissertation 39
 
0.7%
Academic Tutoring 34
 
0.6%
TBC 31
 
0.6%
Research Methods 12
 
0.2%
Research Project 12
 
0.2%
EAP (40 credits) 8
 
0.1%
Research Skills 7
 
0.1%
Intercalating Year Personal Learning Rec 7
 
0.1%
Research Dissertation 6
 
0.1%
Economics 6
 
0.1%
Other values (4381) 5197
97.0%

Length

2023-05-11T21:36:23.283910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 988
 
5.1%
511
 
2.7%
in 432
 
2.2%
of 358
 
1.9%
project 277
 
1.4%
the 269
 
1.4%
research 257
 
1.3%
for 236
 
1.2%
2 204
 
1.1%
1 200
 
1.0%
Other values (2951) 15541
80.6%

Most occurring characters

ValueCountFrequency (%)
13923
 
10.0%
e 11513
 
8.3%
i 10193
 
7.4%
n 10149
 
7.3%
a 9210
 
6.6%
t 8524
 
6.1%
o 8030
 
5.8%
r 7338
 
5.3%
s 6894
 
5.0%
c 5291
 
3.8%
Other values (75) 47542
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 104599
75.5%
Uppercase Letter 17225
 
12.4%
Space Separator 13923
 
10.0%
Other Punctuation 1130
 
0.8%
Decimal Number 950
 
0.7%
Dash Punctuation 266
 
0.2%
Open Punctuation 258
 
0.2%
Close Punctuation 253
 
0.2%
Math Symbol 1
 
< 0.1%
Final Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11513
11.0%
i 10193
9.7%
n 10149
9.7%
a 9210
8.8%
t 8524
 
8.1%
o 8030
 
7.7%
r 7338
 
7.0%
s 6894
 
6.6%
c 5291
 
5.1%
l 4839
 
4.6%
Other values (21) 22618
21.6%
Uppercase Letter
ValueCountFrequency (%)
S 1887
11.0%
P 1852
10.8%
M 1477
 
8.6%
C 1394
 
8.1%
A 1351
 
7.8%
I 1271
 
7.4%
E 1210
 
7.0%
D 1014
 
5.9%
T 842
 
4.9%
L 803
 
4.7%
Other values (16) 4124
23.9%
Decimal Number
ValueCountFrequency (%)
1 342
36.0%
2 302
31.8%
0 78
 
8.2%
3 57
 
6.0%
4 48
 
5.1%
9 33
 
3.5%
6 29
 
3.1%
5 24
 
2.5%
7 19
 
2.0%
8 18
 
1.9%
Other Punctuation
ValueCountFrequency (%)
& 439
38.8%
, 260
23.0%
: 250
22.1%
. 93
 
8.2%
' 42
 
3.7%
/ 32
 
2.8%
; 8
 
0.7%
? 6
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 262
98.5%
4
 
1.5%
Open Punctuation
ValueCountFrequency (%)
( 257
99.6%
[ 1
 
0.4%
Close Punctuation
ValueCountFrequency (%)
) 252
99.6%
] 1
 
0.4%
Space Separator
ValueCountFrequency (%)
13923
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 121824
87.9%
Common 16783
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11513
 
9.5%
i 10193
 
8.4%
n 10149
 
8.3%
a 9210
 
7.6%
t 8524
 
7.0%
o 8030
 
6.6%
r 7338
 
6.0%
s 6894
 
5.7%
c 5291
 
4.3%
l 4839
 
4.0%
Other values (47) 39843
32.7%
Common
ValueCountFrequency (%)
13923
83.0%
& 439
 
2.6%
1 342
 
2.0%
2 302
 
1.8%
- 262
 
1.6%
, 260
 
1.5%
( 257
 
1.5%
) 252
 
1.5%
: 250
 
1.5%
. 93
 
0.6%
Other values (18) 403
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 138590
> 99.9%
None 11
 
< 0.1%
Punctuation 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13923
 
10.0%
e 11513
 
8.3%
i 10193
 
7.4%
n 10149
 
7.3%
a 9210
 
6.6%
t 8524
 
6.2%
o 8030
 
5.8%
r 7338
 
5.3%
s 6894
 
5.0%
c 5291
 
3.8%
Other values (67) 47525
34.3%
Punctuation
ValueCountFrequency (%)
4
66.7%
1
 
16.7%
1
 
16.7%
None
ValueCountFrequency (%)
ó 3
27.3%
é 3
27.3%
à 3
27.3%
í 1
 
9.1%
ñ 1
 
9.1%

MaxCapacity
Real number (ℝ)

Distinct48
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean934.00093
Minimum5
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.0 KiB
2023-05-11T21:36:23.413027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile60
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range994
Interquartile range (IQR)0

Descriptive statistics

Standard deviation239.22691
Coefficient of variation (CV)0.25613134
Kurtosis9.7672068
Mean934.00093
Median Absolute Deviation (MAD)0
Skewness-3.4232546
Sum5005311
Variance57229.513
MonotonicityNot monotonic
2023-05-11T21:36:23.534137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
999 4989
93.1%
20 82
 
1.5%
50 43
 
0.8%
40 33
 
0.6%
30 22
 
0.4%
60 20
 
0.4%
48 18
 
0.3%
100 18
 
0.3%
10 15
 
0.3%
15 12
 
0.2%
Other values (38) 107
 
2.0%
ValueCountFrequency (%)
5 1
 
< 0.1%
10 15
 
0.3%
12 2
 
< 0.1%
14 1
 
< 0.1%
15 12
 
0.2%
16 6
 
0.1%
18 1
 
< 0.1%
20 82
1.5%
24 3
 
0.1%
25 2
 
< 0.1%
ValueCountFrequency (%)
999 4989
93.1%
365 3
 
0.1%
360 2
 
< 0.1%
350 2
 
< 0.1%
280 2
 
< 0.1%
240 1
 
< 0.1%
220 1
 
< 0.1%
200 1
 
< 0.1%
180 2
 
< 0.1%
175 1
 
< 0.1%

IsNew
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
False
5095 
True
 
264
ValueCountFrequency (%)
False 5095
95.1%
True 264
 
4.9%
2023-05-11T21:36:23.657360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

IsDummy
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
False
5311 
True
 
48
ValueCountFrequency (%)
False 5311
99.1%
True 48
 
0.9%
2023-05-11T21:36:23.736432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
True
3185 
False
2174 
ValueCountFrequency (%)
True 3185
59.4%
False 2174
40.6%
2023-05-11T21:36:23.815495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Semester1CreditValue
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1619705
Minimum0
Maximum70
Zeros2238
Zeros (%)41.8%
Negative0
Negative (%)0.0%
Memory size42.0 KiB
2023-05-11T21:36:23.893574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q315
95-th percentile20
Maximum70
Range70
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.812748
Coefficient of variation (CV)1.1801772
Kurtosis6.3244518
Mean9.1619705
Median Absolute Deviation (MAD)10
Skewness1.9830387
Sum49099
Variance116.91553
MonotonicityNot monotonic
2023-05-11T21:36:23.979652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 2238
41.8%
10 1623
30.3%
20 1058
19.7%
15 126
 
2.4%
5 99
 
1.8%
60 77
 
1.4%
30 59
 
1.1%
40 36
 
0.7%
50 22
 
0.4%
3 6
 
0.1%
Other values (7) 15
 
0.3%
ValueCountFrequency (%)
0 2238
41.8%
1 2
 
< 0.1%
3 6
 
0.1%
5 99
 
1.8%
6 3
 
0.1%
10 1623
30.3%
13 2
 
< 0.1%
15 126
 
2.4%
20 1058
19.7%
25 4
 
0.1%
ValueCountFrequency (%)
70 2
 
< 0.1%
60 77
 
1.4%
55 1
 
< 0.1%
50 22
 
0.4%
40 36
 
0.7%
35 1
 
< 0.1%
30 59
 
1.1%
25 4
 
0.1%
20 1058
19.7%
15 126
 
2.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
True
3268 
False
2091 
ValueCountFrequency (%)
True 3268
61.0%
False 2091
39.0%
2023-05-11T21:36:24.260911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Semester2CreditValue
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6969584
Minimum0
Maximum70
Zeros2145
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size42.0 KiB
2023-05-11T21:36:24.338979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q320
95-th percentile20
Maximum70
Range70
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.080872
Coefficient of variation (CV)1.1427162
Kurtosis5.4760812
Mean9.6969584
Median Absolute Deviation (MAD)10
Skewness1.8484721
Sum51966
Variance122.78571
MonotonicityNot monotonic
2023-05-11T21:36:24.431053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 2145
40.0%
10 1568
29.3%
20 1152
21.5%
15 130
 
2.4%
5 109
 
2.0%
60 78
 
1.5%
30 77
 
1.4%
40 47
 
0.9%
50 21
 
0.4%
25 10
 
0.2%
Other values (8) 22
 
0.4%
ValueCountFrequency (%)
0 2145
40.0%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 6
 
0.1%
5 109
 
2.0%
10 1568
29.3%
12 2
 
< 0.1%
15 130
 
2.4%
20 1152
21.5%
25 10
 
0.2%
ValueCountFrequency (%)
70 2
 
< 0.1%
60 78
 
1.5%
55 3
 
0.1%
50 21
 
0.4%
45 2
 
< 0.1%
40 47
 
0.9%
35 4
 
0.1%
30 77
 
1.4%
25 10
 
0.2%
20 1152
21.5%

Semester3Offered
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
False
5053 
True
 
306
ValueCountFrequency (%)
False 5053
94.3%
True 306
 
5.7%
2023-05-11T21:36:24.530143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Semester3CreditValue
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0498227
Minimum0
Maximum70
Zeros5065
Zeros (%)94.5%
Negative0
Negative (%)0.0%
Memory size42.0 KiB
2023-05-11T21:36:24.604210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum70
Range70
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.9701093
Coefficient of variation (CV)4.8638885
Kurtosis25.90044
Mean2.0498227
Median Absolute Deviation (MAD)0
Skewness5.1760999
Sum10985
Variance99.403079
MonotonicityNot monotonic
2023-05-11T21:36:24.692290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 5065
94.5%
60 107
 
2.0%
10 57
 
1.1%
20 31
 
0.6%
50 24
 
0.4%
40 20
 
0.4%
5 16
 
0.3%
30 12
 
0.2%
55 7
 
0.1%
4 6
 
0.1%
Other values (7) 14
 
0.3%
ValueCountFrequency (%)
0 5065
94.5%
1 1
 
< 0.1%
4 6
 
0.1%
5 16
 
0.3%
10 57
 
1.1%
15 4
 
0.1%
20 31
 
0.6%
25 2
 
< 0.1%
30 12
 
0.2%
40 20
 
0.4%
ValueCountFrequency (%)
70 2
 
< 0.1%
68 1
 
< 0.1%
60 107
2.0%
55 7
 
0.1%
54 3
 
0.1%
50 24
 
0.4%
45 1
 
< 0.1%
40 20
 
0.4%
30 12
 
0.2%
25 2
 
< 0.1%

EctsCreditValue
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.46296
Minimum0
Maximum90
Zeros142
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size42.0 KiB
2023-05-11T21:36:24.792381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median10
Q310
95-th percentile30
Maximum90
Range90
Interquartile range (IQR)5

Descriptive statistics

Standard deviation8.7053762
Coefficient of variation (CV)0.83201853
Kurtosis14.910197
Mean10.46296
Median Absolute Deviation (MAD)0
Skewness3.419522
Sum56071
Variance75.783575
MonotonicityNot monotonic
2023-05-11T21:36:24.882463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
10 2696
50.3%
5 1619
30.2%
20 214
 
4.0%
30 179
 
3.3%
8 148
 
2.8%
15 143
 
2.7%
0 142
 
2.6%
60 61
 
1.1%
40 52
 
1.0%
3 39
 
0.7%
Other values (12) 66
 
1.2%
ValueCountFrequency (%)
0 142
 
2.6%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 39
 
0.7%
5 1619
30.2%
8 148
 
2.8%
10 2696
50.3%
13 9
 
0.2%
15 143
 
2.7%
20 214
 
4.0%
ValueCountFrequency (%)
90 1
 
< 0.1%
75 1
 
< 0.1%
60 61
 
1.1%
55 4
 
0.1%
50 11
 
0.2%
45 6
 
0.1%
40 52
 
1.0%
35 8
 
0.1%
30 179
3.3%
28 1
 
< 0.1%

FheqLevel
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9387946
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.0 KiB
2023-05-11T21:36:24.964537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q15
median6
Q37
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1553408
Coefficient of variation (CV)0.1945413
Kurtosis-0.39171346
Mean5.9387946
Median Absolute Deviation (MAD)1
Skewness-0.79076249
Sum31826
Variance1.3348124
MonotonicityNot monotonic
2023-05-11T21:36:25.044610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
7 2244
41.9%
6 1426
26.6%
5 881
 
16.4%
4 623
 
11.6%
3 163
 
3.0%
8 22
 
0.4%
ValueCountFrequency (%)
3 163
 
3.0%
4 623
 
11.6%
5 881
 
16.4%
6 1426
26.6%
7 2244
41.9%
8 22
 
0.4%
ValueCountFrequency (%)
8 22
 
0.4%
7 2244
41.9%
6 1426
26.6%
5 881
 
16.4%
4 623
 
11.6%
3 163
 
3.0%

Mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing12
Missing (%)0.2%
Memory size42.0 KiB
L
4726 
B
621 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5347
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowL
2nd rowL
3rd rowL
4th rowL
5th rowL

Common Values

ValueCountFrequency (%)
L 4726
88.2%
B 621
 
11.6%
(Missing) 12
 
0.2%

Length

2023-05-11T21:36:25.131689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-11T21:36:25.220779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
l 4726
88.4%
b 621
 
11.6%

Most occurring characters

ValueCountFrequency (%)
L 4726
88.4%
B 621
 
11.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5347
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 4726
88.4%
B 621
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5347
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 4726
88.4%
B 621
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 4726
88.4%
B 621
 
11.6%

Delivery
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
ST
4835 
BL
 
445
DEL
 
55
DL
 
12
EL
 
12

Length

Max length3
Median length2
Mean length2.0102631
Min length2

Characters and Unicode

Total characters10773
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowST
2nd rowST
3rd rowST
4th rowST
5th rowST

Common Values

ValueCountFrequency (%)
ST 4835
90.2%
BL 445
 
8.3%
DEL 55
 
1.0%
DL 12
 
0.2%
EL 12
 
0.2%

Length

2023-05-11T21:36:25.299850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-11T21:36:25.398941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
st 4835
90.2%
bl 445
 
8.3%
del 55
 
1.0%
dl 12
 
0.2%
el 12
 
0.2%

Most occurring characters

ValueCountFrequency (%)
S 4835
44.9%
T 4835
44.9%
L 524
 
4.9%
B 445
 
4.1%
D 67
 
0.6%
E 67
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10773
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 4835
44.9%
T 4835
44.9%
L 524
 
4.9%
B 445
 
4.1%
D 67
 
0.6%
E 67
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 10773
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 4835
44.9%
T 4835
44.9%
L 524
 
4.9%
B 445
 
4.1%
D 67
 
0.6%
E 67
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 4835
44.9%
T 4835
44.9%
L 524
 
4.9%
B 445
 
4.1%
D 67
 
0.6%
E 67
 
0.6%

StandAloneAvailability
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing3
Missing (%)0.1%
Memory size42.0 KiB
N
4903 
D
 
410
S
 
43

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5356
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 4903
91.5%
D 410
 
7.7%
S 43
 
0.8%
(Missing) 3
 
0.1%

Length

2023-05-11T21:36:25.487013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-11T21:36:25.576094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
n 4903
91.5%
d 410
 
7.7%
s 43
 
0.8%

Most occurring characters

ValueCountFrequency (%)
N 4903
91.5%
D 410
 
7.7%
S 43
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5356
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 4903
91.5%
D 410
 
7.7%
S 43
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 5356
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 4903
91.5%
D 410
 
7.7%
S 43
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 4903
91.5%
D 410
 
7.7%
S 43
 
0.8%

IsOffered
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
True
3329 
False
2030 
ValueCountFrequency (%)
True 3329
62.1%
False 2030
37.9%
2023-05-11T21:36:25.658168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
True
5359 
ValueCountFrequency (%)
True 5359
100.0%
2023-05-11T21:36:25.736239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

DateFtlcApproved
Categorical

HIGH CARDINALITY  MISSING 

Distinct194
Distinct (%)4.1%
Missing654
Missing (%)12.2%
Memory size42.0 KiB
02-02-2022
604 
08-02-2022
561 
13-02-2022
388 
27-01-2022
328 
10-02-2022
293 
Other values (189)
2531 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters47050
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)1.2%

Sample

1st row02-02-2022
2nd row02-02-2022
3rd row02-02-2022
4th row02-02-2022
5th row02-02-2022

Common Values

ValueCountFrequency (%)
02-02-2022 604
 
11.3%
08-02-2022 561
 
10.5%
13-02-2022 388
 
7.2%
27-01-2022 328
 
6.1%
10-02-2022 293
 
5.5%
28-01-2022 289
 
5.4%
31-01-2022 221
 
4.1%
07-03-2022 178
 
3.3%
26-01-2022 177
 
3.3%
07-02-2022 155
 
2.9%
Other values (184) 1511
28.2%
(Missing) 654
12.2%

Length

2023-05-11T21:36:25.807303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
02-02-2022 604
 
12.8%
08-02-2022 561
 
11.9%
13-02-2022 388
 
8.2%
27-01-2022 328
 
7.0%
10-02-2022 293
 
6.2%
28-01-2022 289
 
6.1%
31-01-2022 221
 
4.7%
07-03-2022 178
 
3.8%
26-01-2022 177
 
3.8%
07-02-2022 155
 
3.3%
Other values (184) 1511
32.1%

Most occurring characters

ValueCountFrequency (%)
2 18466
39.2%
0 11613
24.7%
- 9410
20.0%
1 3100
 
6.6%
3 1581
 
3.4%
8 1095
 
2.3%
7 740
 
1.6%
6 319
 
0.7%
4 315
 
0.7%
9 209
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37640
80.0%
Dash Punctuation 9410
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 18466
49.1%
0 11613
30.9%
1 3100
 
8.2%
3 1581
 
4.2%
8 1095
 
2.9%
7 740
 
2.0%
6 319
 
0.8%
4 315
 
0.8%
9 209
 
0.6%
5 202
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 9410
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47050
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 18466
39.2%
0 11613
24.7%
- 9410
20.0%
1 3100
 
6.6%
3 1581
 
3.4%
8 1095
 
2.3%
7 740
 
1.6%
6 319
 
0.7%
4 315
 
0.7%
9 209
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 18466
39.2%
0 11613
24.7%
- 9410
20.0%
1 3100
 
6.6%
3 1581
 
3.4%
8 1095
 
2.3%
7 740
 
1.6%
6 319
 
0.7%
4 315
 
0.7%
9 209
 
0.4%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
True
5359 
ValueCountFrequency (%)
True 5359
100.0%
2023-05-11T21:36:25.893381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

DateBosApproved
Categorical

HIGH CARDINALITY  MISSING 

Distinct261
Distinct (%)5.3%
Missing478
Missing (%)8.9%
Memory size42.0 KiB
08-02-2022
452 
02-02-2022
 
336
18-01-2022
 
254
13-02-2022
 
220
28-01-2022
 
211
Other values (256)
3408 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters48810
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)1.4%

Sample

1st row19-01-2022
2nd row19-01-2022
3rd row19-01-2022
4th row21-09-2022
5th row13-01-2022

Common Values

ValueCountFrequency (%)
08-02-2022 452
 
8.4%
02-02-2022 336
 
6.3%
18-01-2022 254
 
4.7%
13-02-2022 220
 
4.1%
28-01-2022 211
 
3.9%
31-01-2022 200
 
3.7%
10-02-2022 191
 
3.6%
27-01-2022 190
 
3.5%
20-01-2022 190
 
3.5%
07-02-2022 154
 
2.9%
Other values (251) 2483
46.3%
(Missing) 478
 
8.9%

Length

2023-05-11T21:36:25.965448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
08-02-2022 452
 
9.3%
02-02-2022 336
 
6.9%
18-01-2022 254
 
5.2%
13-02-2022 220
 
4.5%
28-01-2022 211
 
4.3%
31-01-2022 200
 
4.1%
10-02-2022 191
 
3.9%
27-01-2022 190
 
3.9%
20-01-2022 190
 
3.9%
07-02-2022 154
 
3.2%
Other values (251) 2483
50.9%

Most occurring characters

ValueCountFrequency (%)
2 18133
37.2%
0 11578
23.7%
- 9762
20.0%
1 5002
 
10.2%
3 1166
 
2.4%
8 1156
 
2.4%
7 597
 
1.2%
9 502
 
1.0%
4 388
 
0.8%
6 307
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39048
80.0%
Dash Punctuation 9762
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 18133
46.4%
0 11578
29.7%
1 5002
 
12.8%
3 1166
 
3.0%
8 1156
 
3.0%
7 597
 
1.5%
9 502
 
1.3%
4 388
 
1.0%
6 307
 
0.8%
5 219
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 9762
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48810
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 18133
37.2%
0 11578
23.7%
- 9762
20.0%
1 5002
 
10.2%
3 1166
 
2.4%
8 1156
 
2.4%
7 597
 
1.2%
9 502
 
1.0%
4 388
 
0.8%
6 307
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 18133
37.2%
0 11578
23.7%
- 9762
20.0%
1 5002
 
10.2%
3 1166
 
2.4%
8 1156
 
2.4%
7 597
 
1.2%
9 502
 
1.0%
4 388
 
0.8%
6 307
 
0.6%

IsUploadedToSap
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
True
5119 
False
 
240
ValueCountFrequency (%)
True 5119
95.5%
False 240
 
4.5%
2023-05-11T21:36:26.055529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

DateSapUploaded
Categorical

HIGH CARDINALITY  MISSING 

Distinct505
Distinct (%)9.6%
Missing123
Missing (%)2.3%
Memory size42.0 KiB
14-02-2023
491 
09-03-2020
 
315
07-05-2021
 
219
28-01-2022
 
179
10-02-2022
 
161
Other values (500)
3871 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters52360
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique174 ?
Unique (%)3.3%

Sample

1st row14-02-2023
2nd row14-02-2023
3rd row14-02-2023
4th row14-02-2023
5th row14-02-2023

Common Values

ValueCountFrequency (%)
14-02-2023 491
 
9.2%
09-03-2020 315
 
5.9%
07-05-2021 219
 
4.1%
28-01-2022 179
 
3.3%
10-02-2022 161
 
3.0%
02-02-2022 158
 
2.9%
16-03-2021 145
 
2.7%
15-03-2021 122
 
2.3%
09-05-2021 86
 
1.6%
07-03-2022 86
 
1.6%
Other values (495) 3274
61.1%
(Missing) 123
 
2.3%

Length

2023-05-11T21:36:26.131598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14-02-2023 491
 
9.4%
09-03-2020 315
 
6.0%
07-05-2021 219
 
4.2%
28-01-2022 179
 
3.4%
10-02-2022 161
 
3.1%
02-02-2022 158
 
3.0%
16-03-2021 145
 
2.8%
15-03-2021 122
 
2.3%
09-05-2021 86
 
1.6%
07-03-2022 86
 
1.6%
Other values (495) 3274
62.5%

Most occurring characters

ValueCountFrequency (%)
2 15763
30.1%
0 13484
25.8%
- 10472
20.0%
1 4765
 
9.1%
3 2684
 
5.1%
9 1177
 
2.2%
4 1015
 
1.9%
7 826
 
1.6%
5 792
 
1.5%
8 724
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41888
80.0%
Dash Punctuation 10472
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15763
37.6%
0 13484
32.2%
1 4765
 
11.4%
3 2684
 
6.4%
9 1177
 
2.8%
4 1015
 
2.4%
7 826
 
2.0%
5 792
 
1.9%
8 724
 
1.7%
6 658
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 10472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 52360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 15763
30.1%
0 13484
25.8%
- 10472
20.0%
1 4765
 
9.1%
3 2684
 
5.1%
9 1177
 
2.2%
4 1015
 
1.9%
7 826
 
1.6%
5 792
 
1.5%
8 724
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 15763
30.1%
0 13484
25.8%
- 10472
20.0%
1 4765
 
9.1%
3 2684
 
5.1%
9 1177
 
2.2%
4 1015
 
1.9%
7 826
 
1.6%
5 792
 
1.5%
8 724
 
1.4%

PreRequisiteComment
Categorical

HIGH CARDINALITY  MISSING 

Distinct1008
Distinct (%)54.8%
Missing3519
Missing (%)65.7%
Memory size42.0 KiB
None
400 
Students with prior knowledge may seek exemption.
 
34
none
 
27
-
 
26
NONE
 
19
Other values (1003)
1334 

Length

Max length1996
Median length468
Mean length103.86141
Min length1

Characters and Unicode

Total characters191105
Distinct characters90
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique811 ?
Unique (%)44.1%

Sample

1st rowEquivalent Stage 1 Maths Statistics module
2nd rowSufficient Knowledge of Maths and Stats and Introductory Finance.
3rd rowECO1017, or equivalent modules
4th rowNone
5th rowAn understanding of financial Accounting is required for the module provided by ACC1010

Common Values

ValueCountFrequency (%)
None 400
 
7.5%
Students with prior knowledge may seek exemption. 34
 
0.6%
none 27
 
0.5%
- 26
 
0.5%
NONE 19
 
0.4%
English Language to IELTS 6.0 or Pearsons 54 or equivalent. Satisfy progression or admission requirement for entry to Stage 2 on engineering degree programme by satisfactory completion of Stage 1 or equivalent at Level 4 normally with one year of prior study related to this topic. 10
 
0.2%
MAS2502 acceptable in place of MAS2602 10
 
0.2%
IELTS 5.5 (or equivalent) with minimum 5.0 in all four skills (writing, reading, speaking and listening) 9
 
0.2%
None. 9
 
0.2%
Bachelor Degree (or equivalent accredited prior learning) preferably in engineering (for awareness of technical design and regulatory frameworks including Health and Safety) but (subject to specific approval) in cognate applied physical or environmental sciences or technology. For non-native speakers, evidence of English language competence (to a minimum of IELTS 6.5 or equivalent, with higher levels of attainment strongly recommended). 8
 
0.1%
Other values (998) 1288
 
24.0%
(Missing) 3519
65.7%

Length

2023-05-11T21:36:26.256711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of 1079
 
3.7%
the 931
 
3.2%
or 911
 
3.1%
to 800
 
2.7%
and 750
 
2.6%
students 692
 
2.4%
in 637
 
2.2%
module 609
 
2.1%
equivalent 478
 
1.6%
none 460
 
1.6%
Other values (2641) 21850
74.8%

Most occurring characters

ValueCountFrequency (%)
27301
14.3%
e 19697
 
10.3%
t 12363
 
6.5%
o 11332
 
5.9%
n 10979
 
5.7%
a 10746
 
5.6%
i 10527
 
5.5%
s 9167
 
4.8%
r 9134
 
4.8%
l 7083
 
3.7%
Other values (80) 62776
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 144419
75.6%
Space Separator 27301
 
14.3%
Uppercase Letter 9805
 
5.1%
Decimal Number 4758
 
2.5%
Other Punctuation 2675
 
1.4%
Control 728
 
0.4%
Dash Punctuation 459
 
0.2%
Close Punctuation 458
 
0.2%
Open Punctuation 448
 
0.2%
Final Punctuation 32
 
< 0.1%
Other values (2) 22
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 19697
13.6%
t 12363
 
8.6%
o 11332
 
7.8%
n 10979
 
7.6%
a 10746
 
7.4%
i 10527
 
7.3%
s 9167
 
6.3%
r 9134
 
6.3%
l 7083
 
4.9%
d 6092
 
4.2%
Other values (16) 37299
25.8%
Uppercase Letter
ValueCountFrequency (%)
S 1541
15.7%
E 940
 
9.6%
N 746
 
7.6%
A 708
 
7.2%
L 697
 
7.1%
C 695
 
7.1%
M 683
 
7.0%
P 558
 
5.7%
T 444
 
4.5%
G 397
 
4.0%
Other values (16) 2396
24.4%
Other Punctuation
ValueCountFrequency (%)
. 1524
57.0%
, 753
28.1%
/ 150
 
5.6%
: 85
 
3.2%
% 45
 
1.7%
& 41
 
1.5%
; 41
 
1.5%
' 18
 
0.7%
* 8
 
0.3%
6
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 1180
24.8%
1 1030
21.6%
2 902
19.0%
3 333
 
7.0%
5 331
 
7.0%
4 286
 
6.0%
6 231
 
4.9%
8 211
 
4.4%
9 131
 
2.8%
7 123
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 449
97.8%
9
 
2.0%
1
 
0.2%
Control
ValueCountFrequency (%)
352
48.4%
352
48.4%
24
 
3.3%
Close Punctuation
ValueCountFrequency (%)
) 455
99.3%
] 3
 
0.7%
Open Punctuation
ValueCountFrequency (%)
( 445
99.3%
[ 3
 
0.7%
Final Punctuation
ValueCountFrequency (%)
28
87.5%
4
 
12.5%
Math Symbol
ValueCountFrequency (%)
+ 11
91.7%
= 1
 
8.3%
Initial Punctuation
ValueCountFrequency (%)
6
60.0%
4
40.0%
Space Separator
ValueCountFrequency (%)
27301
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 154224
80.7%
Common 36881
 
19.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 19697
12.8%
t 12363
 
8.0%
o 11332
 
7.3%
n 10979
 
7.1%
a 10746
 
7.0%
i 10527
 
6.8%
s 9167
 
5.9%
r 9134
 
5.9%
l 7083
 
4.6%
d 6092
 
4.0%
Other values (42) 47104
30.5%
Common
ValueCountFrequency (%)
27301
74.0%
. 1524
 
4.1%
0 1180
 
3.2%
1 1030
 
2.8%
2 902
 
2.4%
, 753
 
2.0%
) 455
 
1.2%
- 449
 
1.2%
( 445
 
1.2%
352
 
1.0%
Other values (28) 2490
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 191047
> 99.9%
Punctuation 58
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27301
14.3%
e 19697
 
10.3%
t 12363
 
6.5%
o 11332
 
5.9%
n 10979
 
5.7%
a 10746
 
5.6%
i 10527
 
5.5%
s 9167
 
4.8%
r 9134
 
4.8%
l 7083
 
3.7%
Other values (73) 62718
32.8%
Punctuation
ValueCountFrequency (%)
28
48.3%
9
 
15.5%
6
 
10.3%
6
 
10.3%
4
 
6.9%
4
 
6.9%
1
 
1.7%

CoRequisiteComment
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct254
Distinct (%)23.0%
Missing4255
Missing (%)79.4%
Memory size42.0 KiB
None
678 
-
 
44
none
 
40
NONE
 
20
Non-native speakers of English whose current level of attainment is less than UELA 70 or IELTS 7.0 (or recognised equivalent) in all four aspects of communication (Listening, Speaking, Reading, Writing) should be attending the non-credit-bearing in-sessional English language support classes provided by the University.
 
19
Other values (249)
303 

Length

Max length1849
Median length4
Mean length43.531703
Min length1

Characters and Unicode

Total characters48059
Distinct characters81
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique219 ?
Unique (%)19.8%

Sample

1st rowNone
2nd rowNon
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 678
 
12.7%
- 44
 
0.8%
none 40
 
0.7%
NONE 20
 
0.4%
Non-native speakers of English whose current level of attainment is less than UELA 70 or IELTS 7.0 (or recognised equivalent) in all four aspects of communication (Listening, Speaking, Reading, Writing) should be attending the non-credit-bearing in-sessional English language support classes provided by the University. 19
 
0.4%
None. 14
 
0.3%
Non-native speakers of English whose current level of attainment is less than UELA 70 or IELTS 7.0 (or recognised equivalent) in all four aspects of communication (Listening, Speaking, Reading, Writing) should be attending the non- credit-bearing in-sessional English language support classes provided by the University. 6
 
0.1%
Attendance at Tutorials as appropriate 5
 
0.1%
no 4
 
0.1%
SFY0001 Basic Mathematics if below Grade C in GCSE Mathematics 3
 
0.1%
Other values (244) 271
 
5.1%
(Missing) 4255
79.4%

Length

2023-05-11T21:36:26.409850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 752
 
9.9%
the 254
 
3.3%
of 229
 
3.0%
in 187
 
2.5%
students 184
 
2.4%
and 165
 
2.2%
to 160
 
2.1%
module 146
 
1.9%
or 133
 
1.7%
this 107
 
1.4%
Other values (1170) 5293
69.6%

Most occurring characters

ValueCountFrequency (%)
6453
13.4%
e 5011
 
10.4%
n 3550
 
7.4%
o 3171
 
6.6%
t 2998
 
6.2%
i 2682
 
5.6%
s 2523
 
5.2%
a 2401
 
5.0%
r 1961
 
4.1%
l 1616
 
3.4%
Other values (71) 15693
32.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35571
74.0%
Space Separator 6453
 
13.4%
Uppercase Letter 3344
 
7.0%
Decimal Number 1414
 
2.9%
Other Punctuation 616
 
1.3%
Dash Punctuation 220
 
0.5%
Control 180
 
0.4%
Open Punctuation 127
 
0.3%
Close Punctuation 125
 
0.3%
Final Punctuation 6
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5011
14.1%
n 3550
10.0%
o 3171
 
8.9%
t 2998
 
8.4%
i 2682
 
7.5%
s 2523
 
7.1%
a 2401
 
6.7%
r 1961
 
5.5%
l 1616
 
4.5%
d 1414
 
4.0%
Other values (15) 8244
23.2%
Uppercase Letter
ValueCountFrequency (%)
N 839
25.1%
S 389
11.6%
E 281
 
8.4%
L 198
 
5.9%
M 197
 
5.9%
A 171
 
5.1%
T 159
 
4.8%
C 136
 
4.1%
G 127
 
3.8%
P 118
 
3.5%
Other values (14) 729
21.8%
Decimal Number
ValueCountFrequency (%)
0 389
27.5%
1 241
17.0%
2 216
15.3%
8 154
 
10.9%
4 114
 
8.1%
3 109
 
7.7%
7 74
 
5.2%
9 44
 
3.1%
5 43
 
3.0%
6 30
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 323
52.4%
, 197
32.0%
/ 25
 
4.1%
: 23
 
3.7%
& 18
 
2.9%
; 11
 
1.8%
8
 
1.3%
' 7
 
1.1%
% 3
 
0.5%
? 1
 
0.2%
Control
ValueCountFrequency (%)
83
46.1%
83
46.1%
14
 
7.8%
Dash Punctuation
ValueCountFrequency (%)
- 210
95.5%
10
 
4.5%
Final Punctuation
ValueCountFrequency (%)
5
83.3%
1
 
16.7%
Space Separator
ValueCountFrequency (%)
6453
100.0%
Open Punctuation
ValueCountFrequency (%)
( 127
100.0%
Close Punctuation
ValueCountFrequency (%)
) 125
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38915
81.0%
Common 9144
 
19.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5011
12.9%
n 3550
 
9.1%
o 3171
 
8.1%
t 2998
 
7.7%
i 2682
 
6.9%
s 2523
 
6.5%
a 2401
 
6.2%
r 1961
 
5.0%
l 1616
 
4.2%
d 1414
 
3.6%
Other values (39) 11588
29.8%
Common
ValueCountFrequency (%)
6453
70.6%
0 389
 
4.3%
. 323
 
3.5%
1 241
 
2.6%
2 216
 
2.4%
- 210
 
2.3%
, 197
 
2.2%
8 154
 
1.7%
( 127
 
1.4%
) 125
 
1.4%
Other values (22) 709
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48034
99.9%
Punctuation 25
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6453
13.4%
e 5011
 
10.4%
n 3550
 
7.4%
o 3171
 
6.6%
t 2998
 
6.2%
i 2682
 
5.6%
s 2523
 
5.3%
a 2401
 
5.0%
r 1961
 
4.1%
l 1616
 
3.4%
Other values (66) 15668
32.6%
Punctuation
ValueCountFrequency (%)
10
40.0%
8
32.0%
5
20.0%
1
 
4.0%
1
 
4.0%

Availability
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
E
5260 
A
 
51
I
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5359
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowE
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
E 5260
98.2%
A 51
 
1.0%
I 48
 
0.9%

Length

2023-05-11T21:36:26.523953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-11T21:36:26.615037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
e 5260
98.2%
a 51
 
1.0%
i 48
 
0.9%

Most occurring characters

ValueCountFrequency (%)
E 5260
98.2%
A 51
 
1.0%
I 48
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5359
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 5260
98.2%
A 51
 
1.0%
I 48
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 5359
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 5260
98.2%
A 51
 
1.0%
I 48
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 5260
98.2%
A 51
 
1.0%
I 48
 
0.9%

Aims
Categorical

HIGH CARDINALITY  MISSING 

Distinct4570
Distinct (%)87.1%
Missing115
Missing (%)2.1%
Memory size42.0 KiB
This is a module delivered at the University of Groningen for students on programmes 4120F, 4107F and 4018F.
 
62
Original Summary:
 
33
Module for use by Newcastle students on outgoing Study Abroad placement. Therefore there is no specific module content.
 
13
Module for use by Newcastle students on outgoing Erasmus Exchange placement. Therefore there is no specific module content.
 
10
In consonance with the overall aims of the degrees offered in SML, this module will: - build on language skills gained at Stages 1, 2 and 3. - provide students with an in-depth knowledge of the target language and with the ability to develop a high level of written proficiency in professional and academic environments. - prepare students for postgraduate study in areas that make extensive use of the target language. - develop students' written translation skills into the target language.
 
9
Other values (4565)
5117 

Length

Max length4000
Median length1558
Mean length786.73856
Min length17

Characters and Unicode

Total characters4125657
Distinct characters117
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4123 ?
Unique (%)78.6%

Sample

1st rowTo introduce students to economic issues and decision-making tools, relating to major topics like demand analysis and estimation, production and cost functions, and decision making with differing market structures.
2nd row(a) To provide a framework of the methods and techniques of management accounting and control. (b) To provide a framework for an understanding of the design and operation of management accounting and control systems by considering conceptual and practical issues involved.
3rd row1. To examine current financial reporting practice and how it impacts upon companies 2. To enable students to develop accounts preparation and interpretation skills 3. To provide an introduction to group accounts preparation This is an intermediate financial reporting module. Most of the examples relate to companies reporting to shareholders. We shall consider how to account for a range of situations, including accounting for provisions, contingent liabilities and contingent assets, financial instruments and consolidation of group companies with reference to international accounting standards. The module will provide an introduction to the regulatory and IASB frameworks governing the production of financial statements.
4th rowThis module aims for develop an understanding of responsible corporate finance by examining analytical frameworks for the knowledge of the firm's major financing decisions by considering the theoretical models that explain these decisions. The module also examines Finance and Professional Codes of Conduct & Ethics, using real world examples to highlight professional behaviours and the changing ESG environment that accountants and treasurers are now expected to operate in.
5th rowTo provide an analytical framework for the knowledge of the firm's major financing decisions by considering the theoretical models that explain these decisions.

Common Values

ValueCountFrequency (%)
This is a module delivered at the University of Groningen for students on programmes 4120F, 4107F and 4018F. 62
 
1.2%
Original Summary: 33
 
0.6%
Module for use by Newcastle students on outgoing Study Abroad placement. Therefore there is no specific module content. 13
 
0.2%
Module for use by Newcastle students on outgoing Erasmus Exchange placement. Therefore there is no specific module content. 10
 
0.2%
In consonance with the overall aims of the degrees offered in SML, this module will: - build on language skills gained at Stages 1, 2 and 3. - provide students with an in-depth knowledge of the target language and with the ability to develop a high level of written proficiency in professional and academic environments. - prepare students for postgraduate study in areas that make extensive use of the target language. - develop students' written translation skills into the target language. 9
 
0.2%
This is a dummy module is for administrative purposes only, and should only be selected as a placeholder for off regulation choices during the pre-registration period. 8
 
0.1%
The aims of the module are to help students adjust to postgraduate studies. Tutors will emphasise use the sessions to cover topics such as: 1. give advice and answer questions regarding how to read and prepare for seminar at a postgraduate level 2. how postgraduate writing differs from undergraduate writing 3. how to work effectively with dissertation supervisors 4. planning a career, including how to make best use of the Careers Service 5. thinking about a PhD or further study 7
 
0.1%
This module is designed to help international students develop competence in and awareness of academic English in order to successfully start an undergraduate degree programme in the UK. It is specifically designed for students starting the module at an intermediate English language level (approximately IELTS 5.5, CEFR B2). This enhanced 40 credit module is designed to provide students with extensive practice and development time. The module’s specific aims are: • To develop skills and knowledge in academic writing to meet the expectations of the undergraduate academic community at UK HEIs • To develop strategies for reading academic texts in English in order to exploit key text content for further study purposes including written work, seminar discussion and oral seminar presentation • To introduce note-taking skills and to extend this into summarising of key ideas from written and oral texts • To develop an awareness of how secondary sources are to be integrated into student writing so that recognition and respect of source origins are demonstrated and plagiarism is avoided • To raise awareness of academic lecture structure and the strategies required in order to follow and understand lecture content, lecturer behaviour, and lecturer discourse choices • To develop spoken English skills for social and academic spoken interactional contexts, such as seminar discussions, oral presentations, 1-1 tutorials, group work discussions, and informal conversations. • To further extend knowledge and awareness of English grammar, vocabulary and pronunciation features so that academic and socio/cultural assimilation can easily occur 6
 
0.1%
To extend and nuance the range of skills and subject matter of students writing creatively and to encourage formal and thematic experimentation, while allowing students the framework in which to reflect critically and creatively on their own and other people’s writing. 6
 
0.1%
To provide incoming Erasmus chemistry students with practical research experience and training in a UK university laboratory. 6
 
0.1%
Other values (4560) 5084
94.9%
(Missing) 115
 
2.1%

Length

2023-05-11T21:36:26.727138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 37214
 
6.1%
and 35963
 
5.9%
of 30484
 
5.0%
to 29237
 
4.8%
in 14916
 
2.4%
a 10612
 
1.7%
students 8329
 
1.4%
module 6796
 
1.1%
will 5698
 
0.9%
with 5561
 
0.9%
Other values (17300) 426288
69.8%

Most occurring characters

ValueCountFrequency (%)
596173
14.5%
e 395657
 
9.6%
t 294756
 
7.1%
i 276478
 
6.7%
n 271778
 
6.6%
a 266279
 
6.5%
o 254331
 
6.2%
s 227508
 
5.5%
r 199977
 
4.8%
l 154753
 
3.8%
Other values (107) 1187967
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3335933
80.9%
Space Separator 596174
 
14.5%
Other Punctuation 63763
 
1.5%
Uppercase Letter 54256
 
1.3%
Control 43540
 
1.1%
Decimal Number 13323
 
0.3%
Dash Punctuation 7616
 
0.2%
Close Punctuation 4141
 
0.1%
Open Punctuation 3497
 
0.1%
Final Punctuation 2356
 
0.1%
Other values (5) 1058
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 395657
11.9%
t 294756
 
8.8%
i 276478
 
8.3%
n 271778
 
8.1%
a 266279
 
8.0%
o 254331
 
7.6%
s 227508
 
6.8%
r 199977
 
6.0%
l 154753
 
4.6%
d 150149
 
4.5%
Other values (25) 844267
25.3%
Uppercase Letter
ValueCountFrequency (%)
T 17165
31.6%
S 4966
 
9.2%
I 3881
 
7.2%
A 3387
 
6.2%
C 2765
 
5.1%
E 2721
 
5.0%
M 2302
 
4.2%
P 2116
 
3.9%
B 1584
 
2.9%
D 1407
 
2.6%
Other values (16) 11962
22.0%
Other Punctuation
ValueCountFrequency (%)
. 25978
40.7%
, 25129
39.4%
3852
 
6.0%
; 2957
 
4.6%
: 2592
 
4.1%
/ 1127
 
1.8%
' 1055
 
1.7%
? 553
 
0.9%
& 198
 
0.3%
" 127
 
0.2%
Other values (7) 195
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 3717
27.9%
2 2096
15.7%
0 1853
13.9%
3 1511
11.3%
4 1355
 
10.2%
5 682
 
5.1%
9 599
 
4.5%
8 591
 
4.4%
6 492
 
3.7%
7 427
 
3.2%
Math Symbol
ValueCountFrequency (%)
> 93
61.2%
< 38
25.0%
+ 9
 
5.9%
= 8
 
5.3%
~ 4
 
2.6%
Control
ValueCountFrequency (%)
20350
46.7%
20311
46.6%
2877
 
6.6%
 2
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 7256
95.3%
329
 
4.3%
31
 
0.4%
Close Punctuation
ValueCountFrequency (%)
) 4100
99.0%
] 37
 
0.9%
} 4
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 3464
99.1%
[ 29
 
0.8%
{ 4
 
0.1%
Space Separator
ValueCountFrequency (%)
596173
> 99.9%
  1
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
2143
91.0%
213
 
9.0%
Initial Punctuation
ValueCountFrequency (%)
672
75.7%
216
 
24.3%
Currency Symbol
ValueCountFrequency (%)
£ 6
66.7%
$ 3
33.3%
Modifier Symbol
ValueCountFrequency (%)
^ 4
80.0%
` 1
 
20.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3390189
82.2%
Common 735468
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 395657
11.7%
t 294756
 
8.7%
i 276478
 
8.2%
n 271778
 
8.0%
a 266279
 
7.9%
o 254331
 
7.5%
s 227508
 
6.7%
r 199977
 
5.9%
l 154753
 
4.6%
d 150149
 
4.4%
Other values (51) 898523
26.5%
Common
ValueCountFrequency (%)
596173
81.1%
. 25978
 
3.5%
, 25129
 
3.4%
20350
 
2.8%
20311
 
2.8%
- 7256
 
1.0%
) 4100
 
0.6%
3852
 
0.5%
1 3717
 
0.5%
( 3464
 
0.5%
Other values (46) 25138
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4118146
99.8%
Punctuation 7466
 
0.2%
None 45
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
596173
14.5%
e 395657
 
9.6%
t 294756
 
7.2%
i 276478
 
6.7%
n 271778
 
6.6%
a 266279
 
6.5%
o 254331
 
6.2%
s 227508
 
5.5%
r 199977
 
4.9%
l 154753
 
3.8%
Other values (86) 1180456
28.7%
Punctuation
ValueCountFrequency (%)
3852
51.6%
2143
28.7%
672
 
9.0%
329
 
4.4%
216
 
2.9%
213
 
2.9%
31
 
0.4%
8
 
0.1%
2
 
< 0.1%
None
ValueCountFrequency (%)
é 16
35.6%
· 11
24.4%
£ 6
 
13.3%
á 2
 
4.4%
ò 2
 
4.4%
è 2
 
4.4%
  1
 
2.2%
à 1
 
2.2%
â 1
 
2.2%
å 1
 
2.2%
Other values (2) 2
 
4.4%

OutlineOfSyllabus
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct4498
Distinct (%)89.7%
Missing345
Missing (%)6.4%
Memory size42.0 KiB
Specific topics and materials based on current issues will initially be provided by the lecturer. Subsequently, these will be supplied by students themselves for group practice in group work sessions. Translation into the target language, summarising, and open writing will be practised throughout the year, preparing students for written communication in the target language.
 
12
Independent creative practice with guidance from a supervisor who is necessarily a member of staff; in exceptional circumstances, a student might be supervised by a senior doctoral (registered PhD) student.
 
9
The syllabus content will depend on what topic the student chooses.
 
7
TBA
 
7
The list of research topics available is issued in the summer term of the preceding academic year. Students should discuss the topic they are interested in with staff member supervising the topic.
 
7
Other values (4493)
4972 

Length

Max length4000
Median length1624
Mean length773.8217
Min length3

Characters and Unicode

Total characters3879942
Distinct characters136
Distinct categories17 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4091 ?
Unique (%)81.6%

Sample

1st rowOPTIMISATION: A BRIEF REVIEW CONSUMPTION & DEMAND • Axioms of traditional consumer theory • Quantitative demand analysis • Indifference curves, consumption decisions and demand PRODUCTION, COSTS & SUPPLY • Economic analysis of production • Cost functions • The organization of the firm MARKET STRUCTURE & PERFORMANCE OUTCOMES • Monopoly, price discrimination and welfare • Monopolistic competition • Models of Oligopoly: Cournot; Stackelberg; The kinked demand curve • Game theory MARKET STRUCTURE, FIRM STRATEGY & PERFORMANCE • Empirical evidence on SCP paradigm and concentration - profitability • Resource based view of the Firm • Persistance of profit ANALYSIS OF FIRM STRATEGY • Product differentiation • Advertising • Barriers to entry THEORIES OF THE FIRM • Neoclassical • Behavioural • Transaction-costs perspectives
2nd rowThe module is delivered by means of a series of lectures and seminars. It covers: Research methodologies of management accounting Costing (including ABC) Planning and control systems (including budgeting and beyond budgeting) Performance management system Current issues and transfer pricing
3rd row1. Company Financial Statements 2. The Frameworks of Financial Reporting 3. Preparation of consolidated Group Accounts
4th row1- Scope and nature of corporate finance 2- Valuation of debt and equity 3- Security and portfolio analysis 4- Capital market efficiency 5- Asset pricing models and their applications 6- Raising Capital: Debt and Equity 7- Risk Management: Options, FRA's and Futures 8- Capital structure 9- Dividend policy 10- Finance, ESG and Professional Codes of Conduct & Ethics
5th row1- Scope and nature of corporate finance 2- Valuation of debt and equity 3- Security and portfolio analysis 4- Capital market efficiency 5- Asset pricing models and their applications

Common Values

ValueCountFrequency (%)
Specific topics and materials based on current issues will initially be provided by the lecturer. Subsequently, these will be supplied by students themselves for group practice in group work sessions. Translation into the target language, summarising, and open writing will be practised throughout the year, preparing students for written communication in the target language. 12
 
0.2%
Independent creative practice with guidance from a supervisor who is necessarily a member of staff; in exceptional circumstances, a student might be supervised by a senior doctoral (registered PhD) student. 9
 
0.2%
The syllabus content will depend on what topic the student chooses. 7
 
0.1%
TBA 7
 
0.1%
The list of research topics available is issued in the summer term of the preceding academic year. Students should discuss the topic they are interested in with staff member supervising the topic. 7
 
0.1%
Over the course of the year, students will consider: 1. give advice and answer questions regarding how to read and prepare for seminar at a postgraduate level 2. how postgraduate writing differs from undergraduate writing 3. how to work effectively with dissertation supervisors 4. planning a career, including how to make best use of the Careers Service 5. thinking about a PhD or further study 7
 
0.1%
The exact nature of the project is research group dependent, but will encompass the students applying their existing theoretical skills to solving real-life chemical challenges. The student is hosted in the Principal Investigator’s laboratory and becomes part of the research team. 6
 
0.1%
Over the course of taught sessions and scheduled individual tutorials, students will look at a range of literary devices in order to develop their understanding of the tools and resources available to them as writers. Students will explore potential sources and resources, experiment with a variety of dictions and registers, and have the chance to move towards developing their work in response to seminars, tutorials, and independent study and practice. 6
 
0.1%
Students will undertake a single research project in the field of the degree programme studied. Projects will offer diverse experiences in research techniques and methods across the range of Research activities in the Faculty of Medicine Medical Sciences in collaboration with their own workplace environment. Choice of project rests with the student. From the outset the purpose of the project is to widen the students’ outlook to, and experience of, research. The project is intended to allow students from diverse backgrounds to sample areas of current research that are of interest to them in an active research environment and to help them make an informed choice about future career directions and/or help them progress within their chosen career. Students will be supported by the module leader and if appropriate it is highly recommended that students also have a workplace mentor. 5
 
0.1%
Over the course of taught sessions and scheduled individual tutorials, students will look at a range of poetry and poetic devices in order to develop their understanding of the tools and resources available to them as writers. Students will explore potential poetic sources and resources, experiment with a variety of dictions and registers, and have the chance to move towards developing their work in response to seminars, tutorials, and independent study and practice. Please see Module Guide for detail. 5
 
0.1%
Other values (4488) 4943
92.2%
(Missing) 345
 
6.4%

Length

2023-05-11T21:36:26.881277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 34675
 
6.4%
the 26510
 
4.9%
of 22872
 
4.2%
to 11240
 
2.1%
in 9902
 
1.8%
7412
 
1.4%
a 7183
 
1.3%
will 6186
 
1.1%
5432
 
1.0%
for 4145
 
0.8%
Other values (25944) 408047
75.1%

Most occurring characters

ValueCountFrequency (%)
514880
13.3%
e 345707
 
8.9%
i 265569
 
6.8%
t 254292
 
6.6%
n 252692
 
6.5%
a 244998
 
6.3%
o 230486
 
5.9%
s 214583
 
5.5%
r 193892
 
5.0%
l 142541
 
3.7%
Other values (126) 1220302
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3022294
77.9%
Space Separator 514897
 
13.3%
Uppercase Letter 113453
 
2.9%
Control 94199
 
2.4%
Other Punctuation 86308
 
2.2%
Decimal Number 20498
 
0.5%
Dash Punctuation 12958
 
0.3%
Close Punctuation 6328
 
0.2%
Open Punctuation 5808
 
0.1%
Final Punctuation 2007
 
0.1%
Other values (7) 1192
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 345707
11.4%
i 265569
 
8.8%
t 254292
 
8.4%
n 252692
 
8.4%
a 244998
 
8.1%
o 230486
 
7.6%
s 214583
 
7.1%
r 193892
 
6.4%
l 142541
 
4.7%
c 132863
 
4.4%
Other values (37) 744671
24.6%
Uppercase Letter
ValueCountFrequency (%)
T 13252
11.7%
S 11064
 
9.8%
C 9119
 
8.0%
I 7953
 
7.0%
P 7707
 
6.8%
A 7515
 
6.6%
E 6568
 
5.8%
M 6390
 
5.6%
D 5836
 
5.1%
R 5559
 
4.9%
Other values (19) 32490
28.6%
Other Punctuation
ValueCountFrequency (%)
, 29996
34.8%
. 27595
32.0%
: 8781
 
10.2%
7765
 
9.0%
; 6970
 
8.1%
/ 1967
 
2.3%
? 1022
 
1.2%
' 852
 
1.0%
& 775
 
0.9%
* 361
 
0.4%
Other values (9) 224
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 5293
25.8%
2 3489
17.0%
0 2245
11.0%
3 1991
 
9.7%
4 1821
 
8.9%
5 1356
 
6.6%
9 1206
 
5.9%
6 1196
 
5.8%
8 991
 
4.8%
7 910
 
4.4%
Control
ValueCountFrequency (%)
42671
45.3%
42640
45.3%
8886
 
9.4%
1
 
< 0.1%
 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 150
69.1%
> 35
 
16.1%
= 31
 
14.3%
~ 1
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 11525
88.9%
1417
 
10.9%
16
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 6224
98.4%
] 103
 
1.6%
} 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 5732
98.7%
[ 75
 
1.3%
{ 1
 
< 0.1%
Initial Punctuation
ValueCountFrequency (%)
758
79.0%
198
 
20.6%
« 4
 
0.4%
Space Separator
ValueCountFrequency (%)
514880
> 99.9%
  17
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
1809
90.1%
198
 
9.9%
Modifier Symbol
ValueCountFrequency (%)
^ 7
87.5%
´ 1
 
12.5%
Currency Symbol
ValueCountFrequency (%)
£ 3
100.0%
Other Number
ValueCountFrequency (%)
½ 2
100.0%
Other Symbol
ValueCountFrequency (%)
® 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3135747
80.8%
Common 744195
 
19.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 345707
11.0%
i 265569
 
8.5%
t 254292
 
8.1%
n 252692
 
8.1%
a 244998
 
7.8%
o 230486
 
7.4%
s 214583
 
6.8%
r 193892
 
6.2%
l 142541
 
4.5%
c 132863
 
4.2%
Other values (66) 858124
27.4%
Common
ValueCountFrequency (%)
514880
69.2%
42671
 
5.7%
42640
 
5.7%
, 29996
 
4.0%
. 27595
 
3.7%
- 11525
 
1.5%
8886
 
1.2%
: 8781
 
1.2%
7765
 
1.0%
; 6970
 
0.9%
Other values (50) 42486
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3867527
99.7%
Punctuation 12188
 
0.3%
None 227
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
514880
13.3%
e 345707
 
8.9%
i 265569
 
6.9%
t 254292
 
6.6%
n 252692
 
6.5%
a 244998
 
6.3%
o 230486
 
6.0%
s 214583
 
5.5%
r 193892
 
5.0%
l 142541
 
3.7%
Other values (86) 1207887
31.2%
Punctuation
ValueCountFrequency (%)
7765
63.7%
1809
 
14.8%
1417
 
11.6%
758
 
6.2%
198
 
1.6%
198
 
1.6%
27
 
0.2%
16
 
0.1%
None
ValueCountFrequency (%)
é 46
20.3%
ó 22
 
9.7%
è 20
 
8.8%
  17
 
7.5%
í 15
 
6.6%
á 14
 
6.2%
ü 12
 
5.3%
ô 10
 
4.4%
ö 8
 
3.5%
ç 8
 
3.5%
Other values (22) 55
24.2%

StudyAbroad
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
N
2614 
Y
1614 
C
1131 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5359
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowC
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
N 2614
48.8%
Y 1614
30.1%
C 1131
21.1%

Length

2023-05-11T21:36:27.001386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-11T21:36:27.094471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
n 2614
48.8%
y 1614
30.1%
c 1131
21.1%

Most occurring characters

ValueCountFrequency (%)
N 2614
48.8%
Y 1614
30.1%
C 1131
21.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5359
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 2614
48.8%
Y 1614
30.1%
C 1131
21.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 5359
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 2614
48.8%
Y 1614
30.1%
C 1131
21.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 2614
48.8%
Y 1614
30.1%
C 1131
21.1%

IntendedKnowledgeOutcomes
Categorical

HIGH CARDINALITY  MISSING 

Distinct4371
Distinct (%)86.2%
Missing286
Missing (%)5.3%
Memory size42.0 KiB
All areas of grammar, vocabulary and background knowledge related to the skills listed below.
 
61
By the end of the course, students will have gained knowledge in: - using complex lexical and grammatical structures in a range of advanced discourse types, both spoken and written - synthesising information from a variety of authentic sources, comprising extended aural and written input from a variety of demanding discourse types - extended writing in the target language, focusing particularly on argumentative and synthetic compositions in a formal professional and academic register - understanding, using and evaluating information from authentic audio-visual media sources - giving oral presentations, leading and taking part in critical discussions in the target language on a variety of controversial, topical issues, using appropriate range and register.
 
12
By the end of the course, students will have gained knowledge in: - The use of appropriate sentence and text structures, lexicon and register in a range of advanced discourse types - All areas of grammar, vocabulary and background knowledge related to the skills listed below
 
12
Students will acquire and strengthen their knowledge of a range of forms, techniques and thematic concerns and of a variety of perspectives from which these can be approached.
 
10
On completion of this module, students will have: 1. an understanding of specialist academic study skills 2. a better knowledge of the university’s career resources 3. an enhanced understanding of module materials
 
7
Other values (4366)
4971 

Length

Max length3616
Median length1019
Mean length511.904
Min length26

Characters and Unicode

Total characters2596889
Distinct characters104
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3884 ?
Unique (%)76.6%

Sample

1st rowAt the end of this module students will be able to: • Demonstrate a conceptual understanding of business economics, and distinguish some difference from accounting concepts. • Analyse the economic aspects of the nature of competition and behaviour of firms in an industry, and appraise their implications for the competitive strategies and performance of businesses. • Apply economic techniques in managerial decision making, but also illustrate the limitations implicit in such techniques.
2nd rowBy the end of the module students will be able to : - Demonstrate an understanding of the issues involved in designing and operating management accounting and control systems that require to serve diverse purposes. - Examine current research issues in management accounting.
3rd rowBy the end of the module, students should be able to: Apply the requirements of Company Law and International Financial Reporting Standards concerning the format and content of company financial statements Assess and compare the effects of accounting policy choices on reported income, net assets and capital
4th rowBy the end of the module students will be able to - Demonstrate critical understanding of theories and models in finance, and the way they are developed. - Evaluate the key financial decisions faced by a firm and how theories can inform practice. - Compare different approaches to solve financial problems and the ability to critically evaluate them under different circumstances. - Analyse, summarize and interpret academic research for financial decision making.
5th rowBy the end of the module students will be able to - Demonstrate critical understanding of theories and models in finance, and the way they are developed. - Evaluate the key financial decisions faced by a firm and how theories can inform practice. - Compare different approaches to solve financial problems and the ability to critically evaluate them under different circumstances. - Analyse, summarize and interpret academic research for financial decision making.

Common Values

ValueCountFrequency (%)
All areas of grammar, vocabulary and background knowledge related to the skills listed below. 61
 
1.1%
By the end of the course, students will have gained knowledge in: - using complex lexical and grammatical structures in a range of advanced discourse types, both spoken and written - synthesising information from a variety of authentic sources, comprising extended aural and written input from a variety of demanding discourse types - extended writing in the target language, focusing particularly on argumentative and synthetic compositions in a formal professional and academic register - understanding, using and evaluating information from authentic audio-visual media sources - giving oral presentations, leading and taking part in critical discussions in the target language on a variety of controversial, topical issues, using appropriate range and register. 12
 
0.2%
By the end of the course, students will have gained knowledge in: - The use of appropriate sentence and text structures, lexicon and register in a range of advanced discourse types - All areas of grammar, vocabulary and background knowledge related to the skills listed below 12
 
0.2%
Students will acquire and strengthen their knowledge of a range of forms, techniques and thematic concerns and of a variety of perspectives from which these can be approached. 10
 
0.2%
On completion of this module, students will have: 1. an understanding of specialist academic study skills 2. a better knowledge of the university’s career resources 3. an enhanced understanding of module materials 7
 
0.1%
• know in detail the research area(s) in which the work is based • understand research methods and processes 7
 
0.1%
Understanding of general academic skills required for academic study. Understanding of Economics-specific skills required for academic study. 7
 
0.1%
On completion of this module, students will have developed an advanced knowledge and understanding in one or more of the aspects of their discipline. 7
 
0.1%
On successful completion of this module, learners will be able to demonstrate: • Sound understanding of the relevant discipline areas to which the challenge applies • Appreciation of the interconnectedness of disciplines • Knowledge and understanding of how to apply tools and techniques to realise mutual objectives 6
 
0.1%
Students will acquire and strengthen their knowledge of a range of poetic forms, techniques and thematic concerns and of a variety of perspectives from which these can be approached. Students will gain the considered use of tone, register, structure, genre and audience in their own writing; as well as an understanding of editorial approaches and processes. 5
 
0.1%
Other values (4361) 4939
92.2%
(Missing) 286
 
5.3%

Length

2023-05-11T21:36:27.213578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of 26327
 
6.9%
and 25280
 
6.7%
the 24295
 
6.4%
to 12438
 
3.3%
in 7869
 
2.1%
6139
 
1.6%
a 6100
 
1.6%
understanding 4157
 
1.1%
knowledge 3933
 
1.0%
be 3883
 
1.0%
Other values (11986) 258719
68.2%

Most occurring characters

ValueCountFrequency (%)
359101
13.8%
e 250827
 
9.7%
t 183478
 
7.1%
n 178272
 
6.9%
a 172861
 
6.7%
i 166415
 
6.4%
o 154830
 
6.0%
s 137957
 
5.3%
r 121071
 
4.7%
l 97821
 
3.8%
Other values (94) 774256
29.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2088192
80.4%
Space Separator 359101
 
13.8%
Control 52789
 
2.0%
Other Punctuation 43580
 
1.7%
Uppercase Letter 33383
 
1.3%
Decimal Number 8672
 
0.3%
Dash Punctuation 6065
 
0.2%
Close Punctuation 2456
 
0.1%
Open Punctuation 1652
 
0.1%
Final Punctuation 627
 
< 0.1%
Other values (3) 372
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 250827
12.0%
t 183478
 
8.8%
n 178272
 
8.5%
a 172861
 
8.3%
i 166415
 
8.0%
o 154830
 
7.4%
s 137957
 
6.6%
r 121071
 
5.8%
l 97821
 
4.7%
d 97530
 
4.7%
Other values (23) 527130
25.2%
Uppercase Letter
ValueCountFrequency (%)
A 4483
13.4%
T 4045
12.1%
D 2573
 
7.7%
S 2528
 
7.6%
U 2214
 
6.6%
I 2013
 
6.0%
K 1989
 
6.0%
C 1845
 
5.5%
B 1820
 
5.5%
O 1790
 
5.4%
Other values (16) 8083
24.2%
Other Punctuation
ValueCountFrequency (%)
. 15884
36.4%
, 12894
29.6%
6579
15.1%
: 3631
 
8.3%
; 2933
 
6.7%
/ 861
 
2.0%
' 319
 
0.7%
* 192
 
0.4%
& 154
 
0.4%
? 97
 
0.2%
Other values (4) 36
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 2047
23.6%
2 1816
20.9%
3 1496
17.3%
4 1016
11.7%
5 675
 
7.8%
0 473
 
5.5%
6 418
 
4.8%
7 261
 
3.0%
8 242
 
2.8%
9 228
 
2.6%
Math Symbol
ValueCountFrequency (%)
> 36
49.3%
+ 23
31.5%
< 10
 
13.7%
~ 3
 
4.1%
= 1
 
1.4%
Control
ValueCountFrequency (%)
23645
44.8%
23634
44.8%
5510
 
10.4%
Dash Punctuation
ValueCountFrequency (%)
- 5850
96.5%
203
 
3.3%
12
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 2411
98.2%
] 45
 
1.8%
Open Punctuation
ValueCountFrequency (%)
( 1615
97.8%
[ 37
 
2.2%
Final Punctuation
ValueCountFrequency (%)
564
90.0%
63
 
10.0%
Initial Punctuation
ValueCountFrequency (%)
236
80.0%
59
 
20.0%
Space Separator
ValueCountFrequency (%)
359101
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2121575
81.7%
Common 475314
 
18.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 250827
11.8%
t 183478
 
8.6%
n 178272
 
8.4%
a 172861
 
8.1%
i 166415
 
7.8%
o 154830
 
7.3%
s 137957
 
6.5%
r 121071
 
5.7%
l 97821
 
4.6%
d 97530
 
4.6%
Other values (49) 560513
26.4%
Common
ValueCountFrequency (%)
359101
75.6%
23645
 
5.0%
23634
 
5.0%
. 15884
 
3.3%
, 12894
 
2.7%
6579
 
1.4%
- 5850
 
1.2%
5510
 
1.2%
: 3631
 
0.8%
; 2933
 
0.6%
Other values (35) 15653
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2589142
99.7%
Punctuation 7725
 
0.3%
None 22
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
359101
13.9%
e 250827
 
9.7%
t 183478
 
7.1%
n 178272
 
6.9%
a 172861
 
6.7%
i 166415
 
6.4%
o 154830
 
6.0%
s 137957
 
5.3%
r 121071
 
4.7%
l 97821
 
3.8%
Other values (78) 766509
29.6%
Punctuation
ValueCountFrequency (%)
6579
85.2%
564
 
7.3%
236
 
3.1%
203
 
2.6%
63
 
0.8%
59
 
0.8%
12
 
0.2%
9
 
0.1%
None
ValueCountFrequency (%)
é 9
40.9%
· 7
31.8%
è 1
 
4.5%
ö 1
 
4.5%
ü 1
 
4.5%
á 1
 
4.5%
í 1
 
4.5%
ó 1
 
4.5%

IntendedSkillOutcomes
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct4346
Distinct (%)86.1%
Missing314
Missing (%)5.9%
Memory size42.0 KiB
Students will develop the confidence to experiment with their own work and to produce considered and disciplined revision of their work. They will learn to read and evaluate the work of other writers and to evaluate and develop their own writing in the context of this, as well as to receive and incorporate constructive feedback.
 
10
Development of associated skills in research, critical reading and reasoning, sustained discussion and appropriate presentation of the results.
 
9
To learn adaptability and flexibility in living and studying in a different country.
 
8
Ability to understand good academic practice.
 
7
Subject Specific or Professional Skills 1 Increased confidence in laboratory work Cognitive or Intellectual Skills 2 Able to draw conclusions from results 3 Ability to interpret results and modify future work on the basis of this interpretation Key Skills 4 Able to plan project work effectively 5 Able to find and abstract information from primary research sources 6 Able to record labwork and data accurately 7 Able to communicate the essence of the work and results in written and oral form
 
7
Other values (4341)
5004 

Length

Max length3839
Median length1065
Mean length511.09693
Min length26

Characters and Unicode

Total characters2578484
Distinct characters100
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3814 ?
Unique (%)75.6%

Sample

1st rowAt the end of this module students will be able to: • Develop independent learning to prepare a written report. • Develop quantitative skills to practice the using of statistical software, employ the appropriate models to analyse the data, and interpret the empirical findings. • Analyse different market structures and equilibrium outcomes in each of them.
2nd rowBy the end of the module students will be able to: - Manipulate data into relevant management accounting and control information for planning, decision-making and control - Interpret management accounting and control information
3rd rowBy the end of the module, students should be able to: Prepare and interpret published financial statements of limited companies in accordance with IFRS
4th rowBy the end of the module students will acquire the following skills: - Quantitative skills in financial analysis and projections - Critical discussion with evidence - Effective communication - Solve structured and unstructured problems
5th row1. Financial Analysis 2. Critical evaluation of arguments and evidence 3. Drawing conclusions from problems

Common Values

ValueCountFrequency (%)
Students will develop the confidence to experiment with their own work and to produce considered and disciplined revision of their work. They will learn to read and evaluate the work of other writers and to evaluate and develop their own writing in the context of this, as well as to receive and incorporate constructive feedback. 10
 
0.2%
Development of associated skills in research, critical reading and reasoning, sustained discussion and appropriate presentation of the results. 9
 
0.2%
To learn adaptability and flexibility in living and studying in a different country. 8
 
0.1%
Ability to understand good academic practice. 7
 
0.1%
Subject Specific or Professional Skills 1 Increased confidence in laboratory work Cognitive or Intellectual Skills 2 Able to draw conclusions from results 3 Ability to interpret results and modify future work on the basis of this interpretation Key Skills 4 Able to plan project work effectively 5 Able to find and abstract information from primary research sources 6 Able to record labwork and data accurately 7 Able to communicate the essence of the work and results in written and oral form 7
 
0.1%
On completion of this module, students will have had opportunities to enhance their skills in the generation, interpretation and use of data relevant to their discipline. In particular, they will have: (a) critically assessed the value of data and other information on a topic; (b) formulated or recognised key hypotheses, and identified key data/information which would allow these hypotheses to be tested; (c) in many cases, generated such data, through field, laboratory, or other means, and (d) presented and summarised such data, and critically appraised their significance, using appropriate numerical and other techniques. 7
 
0.1%
Speaking: To develop further the skill of speaking so that by the end of the module students will feel confident about communicating with native speakers. This will go beyond dealing with everyday situations – students will be expected to express their views on more complex topics. Listening: To reinforce the students’ listening skills by regularly listening to (mostly) authentic broadcasts from the radio and television (this could include interviews, reports or film extracts). Writing: To reinforce the students’ ability to write reports, essays and letters in the foreign language. Reading: To reinforce the students’ reading skills through a variety of more complex authentic materials than studied previously in the target language. Gammar/Vocabulary: • To further their command of grammar and to introduce and practise more complex structures • To help expan their vocabulary to areas which go beyond basic “everyday use” of the foreign language 7
 
0.1%
Speaking: Talking about yourself; Asking for/giving directions; Comparing products and services; Socialising. Listening: Students should be able to understand native speakers in situations similar to those listed above, provided that they speak fairly slowly, addressing you directly and with a reasonable clear accent. They will also become familiar with the most common phrases in telephone conversations and be able to achieve a basic level of comprehension when listening to simple off-air material. Writing: Students should be able to write simple compositions based on topics and situations listed above such a short notes, and informal letters to friends, and simple formal letters asking for information. Reading: Students will be able to understand relevant information from short newspaper reports, brochures, letters, basic regulations, publicity materials, etc. Cognitive/intellectual skills: Students will learn: - To understand the functions and structures of a foreign language at Elementary level. - To locate and make use of materials appropriate for this level other than those provided by the teacher. - To evaluate their own performance - To apply a range of strategies for language learning appropriate for this level. 6
 
0.1%
On completion of this module, learners will be able to: • Lead cross-disciplinary teams and demonstrate appreciation of different disciplinary specialisms • Demonstrate curiosity in the pursuit of solutions to grand challenges • Be confident in dealing with ambiguity and uncertainty 6
 
0.1%
Opportunities are afforded to develop the following subject-specific skills: ISO1 - The ability to use library and web-based resources to research the selected topic. ISO2 - The ability to plan and manage a self-directed research or design project. ISO3 - The ability to write a journal paper and associated supporting materials. 5
 
0.1%
Other values (4336) 4973
92.8%
(Missing) 314
 
5.9%

Length

2023-05-11T21:36:27.364725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 25196
 
6.7%
to 18667
 
5.0%
the 15849
 
4.2%
of 15032
 
4.0%
in 8569
 
2.3%
a 7204
 
1.9%
6616
 
1.8%
skills 4969
 
1.3%
4254
 
1.1%
be 4186
 
1.1%
Other values (9369) 264881
70.6%

Most occurring characters

ValueCountFrequency (%)
352229
13.7%
e 242301
 
9.4%
t 183621
 
7.1%
a 177279
 
6.9%
i 174745
 
6.8%
n 167113
 
6.5%
o 145786
 
5.7%
s 137656
 
5.3%
r 125128
 
4.9%
l 113492
 
4.4%
Other values (90) 759134
29.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2063909
80.0%
Space Separator 352229
 
13.7%
Control 60924
 
2.4%
Other Punctuation 46406
 
1.8%
Uppercase Letter 33671
 
1.3%
Decimal Number 8505
 
0.3%
Dash Punctuation 7295
 
0.3%
Close Punctuation 2781
 
0.1%
Open Punctuation 2045
 
0.1%
Final Punctuation 539
 
< 0.1%
Other values (4) 180
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 242301
11.7%
t 183621
 
8.9%
a 177279
 
8.6%
i 174745
 
8.5%
n 167113
 
8.1%
o 145786
 
7.1%
s 137656
 
6.7%
r 125128
 
6.1%
l 113492
 
5.5%
c 83938
 
4.1%
Other values (17) 512850
24.8%
Uppercase Letter
ValueCountFrequency (%)
T 4519
13.4%
S 4400
13.1%
A 4005
11.9%
C 2952
8.8%
D 2450
 
7.3%
I 2403
 
7.1%
O 1837
 
5.5%
B 1782
 
5.3%
E 1616
 
4.8%
P 1537
 
4.6%
Other values (16) 6170
18.3%
Other Punctuation
ValueCountFrequency (%)
. 16565
35.7%
, 13368
28.8%
6990
15.1%
: 4455
 
9.6%
; 3173
 
6.8%
/ 1112
 
2.4%
* 301
 
0.6%
' 177
 
0.4%
& 147
 
0.3%
? 63
 
0.1%
Other values (4) 55
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 1959
23.0%
2 1758
20.7%
3 1439
16.9%
4 1059
12.5%
5 783
 
9.2%
6 524
 
6.2%
7 378
 
4.4%
8 243
 
2.9%
0 199
 
2.3%
9 163
 
1.9%
Math Symbol
ValueCountFrequency (%)
> 38
67.9%
< 10
 
17.9%
+ 4
 
7.1%
~ 4
 
7.1%
Control
ValueCountFrequency (%)
27613
45.3%
27574
45.3%
5737
 
9.4%
Dash Punctuation
ValueCountFrequency (%)
- 7060
96.8%
217
 
3.0%
18
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 2718
97.7%
] 59
 
2.1%
} 4
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1992
97.4%
[ 49
 
2.4%
{ 4
 
0.2%
Final Punctuation
ValueCountFrequency (%)
518
96.1%
21
 
3.9%
Initial Punctuation
ValueCountFrequency (%)
94
83.9%
18
 
16.1%
Space Separator
ValueCountFrequency (%)
352229
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 8
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2097580
81.3%
Common 480904
 
18.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 242301
11.6%
t 183621
 
8.8%
a 177279
 
8.5%
i 174745
 
8.3%
n 167113
 
8.0%
o 145786
 
7.0%
s 137656
 
6.6%
r 125128
 
6.0%
l 113492
 
5.4%
c 83938
 
4.0%
Other values (43) 546521
26.1%
Common
ValueCountFrequency (%)
352229
73.2%
27613
 
5.7%
27574
 
5.7%
. 16565
 
3.4%
, 13368
 
2.8%
- 7060
 
1.5%
6990
 
1.5%
5737
 
1.2%
: 4455
 
0.9%
; 3173
 
0.7%
Other values (37) 16140
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2570567
99.7%
Punctuation 7880
 
0.3%
None 37
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
352229
13.7%
e 242301
 
9.4%
t 183621
 
7.1%
a 177279
 
6.9%
i 174745
 
6.8%
n 167113
 
6.5%
o 145786
 
5.7%
s 137656
 
5.4%
r 125128
 
4.9%
l 113492
 
4.4%
Other values (80) 751217
29.2%
Punctuation
ValueCountFrequency (%)
6990
88.7%
518
 
6.6%
217
 
2.8%
94
 
1.2%
21
 
0.3%
18
 
0.2%
18
 
0.2%
4
 
0.1%
None
ValueCountFrequency (%)
· 34
91.9%
é 3
 
8.1%

GraduateSkillsFrameworkApplicable
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
False
5142 
True
 
217
ValueCountFrequency (%)
False 5142
96.0%
True 217
 
4.0%
2023-05-11T21:36:27.489830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

CriticalThinking
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:27.573906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

DataSynthesis
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:27.656982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

ActiveLearning
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:27.736053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Numeracy
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:27.808118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Literacy
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:27.879183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

SelfAwarenessAndReflection
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:27.949751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

InnovationAndCreativity
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:28.020825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Initiative
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:28.092906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Independence
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:28.163994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Adaptability
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:28.236069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

ProblemSolving
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:28.306133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Budgeting
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:28.377213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Oral
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:28.448292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

ForeignLanguages
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:28.519356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Interpersonal
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:28.590432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

WrittenOther
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:28.661509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Collaboration
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:28.732586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

RelationshipBuilding
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:28.802649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Leadership
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:28.873714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Negotiation
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:28.944779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

PeerAssessmentReview
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:29.015834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

OccupationalAwareness
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:29.086907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

MarketAwareness
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:29.157972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

GovernanceAwareness
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:29.471259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

FinancialAwareness
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:29.542321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

BusinessPlanning
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:29.613385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

EthicalAwareness
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:29.684441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

SocialCulturalGlobalAwareness
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:29.756506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

LegalAwareness
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:29.827580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

SourceMaterials
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:29.898644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

SynthesiseAndPresentMaterials
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:29.969709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

UseOfComputerApplications
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:30.040785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

GoalSettingAndActionPlanning
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:30.111850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

DecisionMaking
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing217
Missing (%)4.0%
Memory size10.6 KiB
False
5142 
(Missing)
 
217
ValueCountFrequency (%)
False 5142
96.0%
(Missing) 217
 
4.0%
2023-05-11T21:36:30.182927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

TeachingRationaleAndRelationship
Categorical

HIGH CARDINALITY  MISSING 

Distinct4098
Distinct (%)81.6%
Missing339
Missing (%)6.3%
Memory size42.0 KiB
Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Problem Classes are used to help develop the students’ abilities at applying the theory to solving problems.
 
68
1. Teaching in the language seminars will focus on communication skills. Grammar will be taught systematically to enable students to produce and manipulate the foreign language. Use of the target language will be made as much as possible to develop listening skills. All four skills: listening, reading, writing and speaking are fully integrated in the language seminars and associated work and preparation, and will be tested equally. 2. Independent learning and learner autonomy are further developed through online guided tasks, pair and group work, assessment preparation and completion, and self-study at home, via the University’s Virtual Learning Environment (VLE) and in the Language Resource Centre. Particular initiative is expected from students, e.g. course participants will be asked to contribute their own materials to the classes and activities. 3. Depending on numbers we may be able to offer one online only group and the Teaching Activities will differ as follows. Scheduled Learning and Teaching Activities / Small group teaching / 11 weeks / 2 hours weekly / 22 hours / Online synchronous language seminars
 
65
This is a dummy MOF set up primarily for HESA purposes. The information in this section may not be current and does not provide the detail required of a normal MOF. This information should not be downloaded and used, instead information in relation to teaching activities and assessments should be sourced by contacting the relevant school.
 
28
Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work.
 
22
Non-synchronous online materials are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on assessed work. Present-in-person and synchronous online sessions are used to help develop the students’ abilities at applying the theory to solving problems and to identify and resolve specific queries raised by students, and to allow students to receive individual feedback on marked work. Students who cannot attend a present-in-person session will be provided with an alternative activity allowing them to access the learning outcomes of that session. In addition, office hours/discussion board activity will provide an opportunity for more direct contact between individual students and the lecturer: a typical student might spend a total of one or two hours over the course of the module, either individually or as part of a group. Alternatives will be offered to students unable to be present-in-person due to the prevailing C-19 circumstances. Student’s should consult their individual timetable for up-to-date delivery information.
 
14
Other values (4093)
4823 

Length

Max length4000
Median length1530
Mean length747.71932
Min length42

Characters and Unicode

Total characters3753551
Distinct characters107
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3628 ?
Unique (%)72.3%

Sample

1st rowFormal lecture materials are used to explain the issues, and to introduce concepts and techniques. Seminar sessions are held throughout the year, designed to be both explanatory and interactive and offering the opportunity to explore issues raised in lectures. Students have the opportunity to develop and practise key skills in these sessions. Students are able to judge their progress in the module through these sessions.
2nd rowLectures materials are used to explain relevant issues and to introduce appropriate concepts and techniques. Seminars are used to enable students to apply and develop skills in an interactive environment.
3rd rowLecture material introduces the course material to students, and concentrates upon some of the more challenging aspects of financial reporting. Students are given a programme of required reading to supplement the lecture materials and are encouraged to attempt questions as well as reading around subjects as widely as possible. The synchronous sessions consolidate the course material by allowing students to tackle problems in a small group environment, where the seminar leader is available to provide explanations and give extra help as required. The practice questions set will help students to develop problem-solving, numeracy and written-communication skills. The synchronous sessions are designed to encourage discussion by probing the implications of alternative accounting policy choices and trends in financial reporting. Students are required to attend synchronous seminar sessions with their attempts at set questions ready to discuss these with the rest of the group.
4th rowLecture materials are designed to provide an introduction and exposition of key models, research and financial decisions Private study enables students to develop this in more detail. Scehduled contact time provides an opportunity for students to work individually and in groups to discuss reading and work through problem questions. Group sessions provide an opportunity for students to develop their problem solving skills
5th rowLectures are designed to provide an introduction and exposition of key models, research and financial decisions [A1] Private study enables students to develop this in more detail. Seminars provide an opportunity for students to work individually and in groups to discuss reading and work through problem questions. [B1-B3]. Group feedback sessions provide an opportunity for students to develop their problem solving skills [B1] - [B3]

Common Values

ValueCountFrequency (%)
Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Problem Classes are used to help develop the students’ abilities at applying the theory to solving problems. 68
 
1.3%
1. Teaching in the language seminars will focus on communication skills. Grammar will be taught systematically to enable students to produce and manipulate the foreign language. Use of the target language will be made as much as possible to develop listening skills. All four skills: listening, reading, writing and speaking are fully integrated in the language seminars and associated work and preparation, and will be tested equally. 2. Independent learning and learner autonomy are further developed through online guided tasks, pair and group work, assessment preparation and completion, and self-study at home, via the University’s Virtual Learning Environment (VLE) and in the Language Resource Centre. Particular initiative is expected from students, e.g. course participants will be asked to contribute their own materials to the classes and activities. 3. Depending on numbers we may be able to offer one online only group and the Teaching Activities will differ as follows. Scheduled Learning and Teaching Activities / Small group teaching / 11 weeks / 2 hours weekly / 22 hours / Online synchronous language seminars 65
 
1.2%
This is a dummy MOF set up primarily for HESA purposes. The information in this section may not be current and does not provide the detail required of a normal MOF. This information should not be downloaded and used, instead information in relation to teaching activities and assessments should be sourced by contacting the relevant school. 28
 
0.5%
Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. 22
 
0.4%
Non-synchronous online materials are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on assessed work. Present-in-person and synchronous online sessions are used to help develop the students’ abilities at applying the theory to solving problems and to identify and resolve specific queries raised by students, and to allow students to receive individual feedback on marked work. Students who cannot attend a present-in-person session will be provided with an alternative activity allowing them to access the learning outcomes of that session. In addition, office hours/discussion board activity will provide an opportunity for more direct contact between individual students and the lecturer: a typical student might spend a total of one or two hours over the course of the module, either individually or as part of a group. Alternatives will be offered to students unable to be present-in-person due to the prevailing C-19 circumstances. Student’s should consult their individual timetable for up-to-date delivery information. 14
 
0.3%
Lectures will be used to introduce the learning material and for demonstrating the key concepts by example. Students are expected to follow-up lectures within a few days by re-reading and annotating lecture notes to aid deep learning. This is a very practical subject, and it is important that the learning materials are supported by hands-on opportunities provided by practical classes. Students are expected to spend time on coursework outside timetabled practical classes. Students aiming for 1st class marks are expected to widen their knowledge beyond the content of lecture notes through background reading. Students should set aside sufficient time to revise for the end of semester exam. 13
 
0.2%
Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Problem Classes are used to help develop the students’ abilities at applying the theory to solving problems. Tutorials are used to identify and resolve specific queries raised by students and to allow students to receive individual feedback on marked work. In addition, office hours (two per week) will provide an opportunity for more direct contact between individual students and the lecturer: a typical student might spend a total of one or two hours over the course of the module, either individually or as part of a group. 12
 
0.2%
Live-synchronous small group tutorials provides students with guidance and direction on their design project assignments. Workshops provide the weekly induction for tasks. Additional recorded lecture material is available for non-synchronous learning and an opportunity to revisit contents. A major part of student hours is dedicated for practical project work to allow progress on design project assignments. Drop-in/surgery provide students an opportunity for personal tutorials. 12
 
0.2%
Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Problem Classes are used to help develop the students’ abilities at applying the theory to solving problems. Tutorials are used to identify and resolve specific queries raised by students and to allow students to receive individual feedback on marked work. In addition, office hours (two per week) will provide an opportunity for more direct contact between individual students and the lecturer. 11
 
0.2%
Language classes will introduce, model and offer guidance in each of the skills that students are required to practise during the course (including summary and commentary writing, intensive reading, taking part in critical discussions, and translation into the foreign language). Group work will offer students an opportunity to work in small groups (in the target language) under close supervision of the lecturer. These classes will focus on the skills listed in the outline syllabus. Teaching will be mostly in the target language. Work requirements: - researching materials for oral presentation and discussion - writing critical discussions and summaries in the target language - practising and revising advanced vocabulary and grammar 9
 
0.2%
Other values (4088) 4766
88.9%
(Missing) 339
 
6.3%

Length

2023-05-11T21:36:30.289106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 32191
 
5.9%
the 32087
 
5.8%
to 21833
 
4.0%
of 17985
 
3.3%
in 11706
 
2.1%
will 11357
 
2.1%
students 10249
 
1.9%
a 7721
 
1.4%
for 6675
 
1.2%
be 6554
 
1.2%
Other values (9866) 391308
71.2%

Most occurring characters

ValueCountFrequency (%)
542969
14.5%
e 386199
 
10.3%
t 270888
 
7.2%
i 245455
 
6.5%
n 238268
 
6.3%
s 227994
 
6.1%
a 224422
 
6.0%
o 213145
 
5.7%
r 196671
 
5.2%
l 150442
 
4.0%
Other values (97) 1057098
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3061808
81.6%
Space Separator 542975
 
14.5%
Other Punctuation 51366
 
1.4%
Uppercase Letter 44503
 
1.2%
Control 26425
 
0.7%
Dash Punctuation 10156
 
0.3%
Decimal Number 7399
 
0.2%
Close Punctuation 3478
 
0.1%
Open Punctuation 3285
 
0.1%
Final Punctuation 1657
 
< 0.1%
Other values (5) 499
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 386199
12.6%
t 270888
 
8.8%
i 245455
 
8.0%
n 238268
 
7.8%
s 227994
 
7.4%
a 224422
 
7.3%
o 213145
 
7.0%
r 196671
 
6.4%
l 150442
 
4.9%
d 148480
 
4.8%
Other values (22) 759844
24.8%
Uppercase Letter
ValueCountFrequency (%)
T 9744
21.9%
S 6828
15.3%
L 3836
 
8.6%
I 3049
 
6.9%
P 2836
 
6.4%
A 2821
 
6.3%
C 2038
 
4.6%
O 1673
 
3.8%
E 1598
 
3.6%
D 1258
 
2.8%
Other values (16) 8822
19.8%
Other Punctuation
ValueCountFrequency (%)
. 25223
49.1%
, 19800
38.5%
/ 1847
 
3.6%
: 1554
 
3.0%
; 1077
 
2.1%
631
 
1.2%
' 571
 
1.1%
& 320
 
0.6%
? 157
 
0.3%
* 70
 
0.1%
Other values (7) 116
 
0.2%
Decimal Number
ValueCountFrequency (%)
1 2286
30.9%
2 1706
23.1%
3 803
 
10.9%
0 737
 
10.0%
4 500
 
6.8%
5 413
 
5.6%
9 300
 
4.1%
6 290
 
3.9%
8 222
 
3.0%
7 142
 
1.9%
Math Symbol
ValueCountFrequency (%)
= 60
75.0%
+ 17
 
21.2%
> 2
 
2.5%
~ 1
 
1.2%
Control
ValueCountFrequency (%)
12959
49.0%
12950
49.0%
516
 
2.0%
Space Separator
ValueCountFrequency (%)
542969
> 99.9%
  6
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 9954
98.0%
202
 
2.0%
Close Punctuation
ValueCountFrequency (%)
) 3463
99.6%
] 15
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 3270
99.5%
[ 15
 
0.5%
Final Punctuation
ValueCountFrequency (%)
1557
94.0%
100
 
6.0%
Initial Punctuation
ValueCountFrequency (%)
311
75.7%
100
 
24.3%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 3
100.0%
Other Number
ValueCountFrequency (%)
½ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3106311
82.8%
Common 647240
 
17.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 386199
12.4%
t 270888
 
8.7%
i 245455
 
7.9%
n 238268
 
7.7%
s 227994
 
7.3%
a 224422
 
7.2%
o 213145
 
6.9%
r 196671
 
6.3%
l 150442
 
4.8%
d 148480
 
4.8%
Other values (48) 804347
25.9%
Common
ValueCountFrequency (%)
542969
83.9%
. 25223
 
3.9%
, 19800
 
3.1%
12959
 
2.0%
12950
 
2.0%
- 9954
 
1.5%
) 3463
 
0.5%
( 3270
 
0.5%
1 2286
 
0.4%
/ 1847
 
0.3%
Other values (39) 12519
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3750624
99.9%
Punctuation 2905
 
0.1%
None 22
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
542969
14.5%
e 386199
 
10.3%
t 270888
 
7.2%
i 245455
 
6.5%
n 238268
 
6.4%
s 227994
 
6.1%
a 224422
 
6.0%
o 213145
 
5.7%
r 196671
 
5.2%
l 150442
 
4.0%
Other values (81) 1054171
28.1%
Punctuation
ValueCountFrequency (%)
1557
53.6%
631
21.7%
311
 
10.7%
202
 
7.0%
100
 
3.4%
100
 
3.4%
4
 
0.1%
None
ValueCountFrequency (%)
  6
27.3%
é 4
18.2%
ñ 3
13.6%
· 3
13.6%
ó 2
 
9.1%
à 1
 
4.5%
½ 1
 
4.5%
á 1
 
4.5%
ô 1
 
4.5%

AssessmentRationaleAndRelationship
Categorical

HIGH CARDINALITY  MISSING 

Distinct4286
Distinct (%)83.5%
Missing224
Missing (%)4.2%
Memory size42.0 KiB
A substantial formal unseen examination is appropriate for the assessment of the material in this module. The coursework assignments allow the students to develop their problem solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback; these assessments have a secondary formative purpose as well as their primary summative purpose. In the event of on-campus examinations not being possible, an on-line alternative assessment will be used for written examination 1.
 
66
The portfolio will help students assess their progress and identify their strengths and areas for improvement. It will test student’s ability to communicate effectively, identify relevant information and produce a structured text in the target language.
 
64
Dummy assessment for SAP upload (see NESS for actual assessment)
 
62
There is no assessment for this module
 
46
A substantial formal examination is appropriate for the assessment of the material in this module. The course assessments will allow the students to develop their problem solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback; these assessments have a secondary formative purpose as well as their primary summative purpose.
 
25
Other values (4281)
4872 

Length

Max length3998
Median length1660
Mean length716.31899
Min length19

Characters and Unicode

Total characters3678298
Distinct characters103
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3890 ?
Unique (%)75.8%

Sample

1st rowFormal examination tests the students' intended knowledge outcomes and their ability to write about specific models/issues and solve numerical problems. The 1200 word report provides an opportunity for students to demonstrate their business knowledge and written communication (report writing) skills. In the case of an alternative semester 2 assessment (worth 75% of the overall module mark) being necessary due to circumstances, the module leader will in the first instance consult with the DPD as to the requirements of the professional accrediting body to discuss possible acceptable alternatives. In 2020/21 this alternative was a 24 hour take home exam delivered online, and it is envisioned that if circumstances do not allow a present-in-person timed exam at the end of semester 2, and the professional body agrees, than this may well be an example of the type of alternative assessment which could be put in place.
2nd rowThe formal examination tests students' intended knowledge and skills outcomes, in particular the framing of data into relevant management accounting and control information and the use and interpretation of this information. The assessment scheme examines students on set problems of management accounting and control systems as applied to planning, decision-making and control. The group project tests students on the application of management accounting and control systems by use of an extended problem/short case incorporating features of real-world complexity. Self and peer review will take place after the report, and individuals will receive the group mark adjusted according to self and peer review i.e. their own and their team members’ assessment of each other’s contributions to report. Each team is to keep a log of its meetings, which should be handed in with the report itself. The module leader retains the right to adjust individual marks where it is deemed necessary in the interests of fairness.
3rd rowThe semester 2 written examination tests the students' intended knowledge and skills outcomes, in particular their ability to write succinct essays and solve numerical problems, covering content from both semesters. The semester 1 MCQ examination will provide an assessment of students' core knowledge of IFRS standards covered in semester 1. In the case of an alternative semester 2 assessment (worth 75% of the overall module mark) being necessary due to circumstances, the module leader will in the first instance consult with the DPD as to the requirements of the professional accrediting body to discuss possible acceptable alternatives. In 2020/21 this alternative was a 24 hour take home exam delivered online, and it is envisioned that if circumstances do not allow a present-in-person timed exam at the end of semester 2, and the professional body agrees, than this may well be an example of the type of alternative assessment which could be put in place.
4th rowThe formal examination assesses the students' intended knowledge and skills outcomes as well as problem solving, numeracy and written communication skills. For 2022/23 onwards it is the intention of the module team to move the Inspera digital exams platform for an in-person digital 3 hour closed book exam. Should this not be possible, the exam will remain as a written 3 hour closed book exam. In the case of an alternative semester 2 assessment being necessary due to circumstances, the module leader will in the first instance consult with the DPD as to the requirements of the professional accrediting body to discuss possible acceptable alternatives. In 2020/21 this alternative was a 24 hour take home exam delivered online, and it is envisioned that if circumstances do not allow a present-in-person timed exam (digital or written) at the end of semester 2, and the professional body agrees, than this may well be an example of the type of alternative assessment which could be put in place.
5th rowThe examination assesses the students' intended knowledge and skills outcomes as well as problem solving and numeracy skills.

Common Values

ValueCountFrequency (%)
A substantial formal unseen examination is appropriate for the assessment of the material in this module. The coursework assignments allow the students to develop their problem solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback; these assessments have a secondary formative purpose as well as their primary summative purpose. In the event of on-campus examinations not being possible, an on-line alternative assessment will be used for written examination 1. 66
 
1.2%
The portfolio will help students assess their progress and identify their strengths and areas for improvement. It will test student’s ability to communicate effectively, identify relevant information and produce a structured text in the target language. 64
 
1.2%
Dummy assessment for SAP upload (see NESS for actual assessment) 62
 
1.2%
There is no assessment for this module 46
 
0.9%
A substantial formal examination is appropriate for the assessment of the material in this module. The course assessments will allow the students to develop their problem solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback; these assessments have a secondary formative purpose as well as their primary summative purpose. 25
 
0.5%
This is a dummy MOF set up primarily for HESA purposes. The information in this section may not be current and does not provide the detail required of a normal MOF. This information should not be downloaded and used, instead information in relation to teaching activities and assessments should be sourced by contacting the relevant school. 19
 
0.4%
The post module assignments are an appropriate way to assess both theoretical knowledge and understanding and problem solving skills under time-constraint as required in industry. They enable a more realistic engineering design problem to be set and will also assess data and information acquisition and evaluation skills. 16
 
0.3%
Students demonstrate acquisition of knowledge and skills through the submission of their creative work and further demonstrate their understanding of their own creative practice through the accompanying essayistic work. 10
 
0.2%
Mode of assessment will vary according to the nature of the project, but will be established and agreed in writing at an early stage in discussions between student and supervisor. Typically it might be a performance or performances, a portfolio of scores or an anthology of recordings or other media products, or some combination of these. An assessment timetable, with a programme of ‘milestones’ (either formative or summative, depending on the nature of the project), will also be agreed. 9
 
0.2%
A substantial formal unseen examination is appropriate for the assessment of the material in this module. The coursework assignments allow the students to develop their problem solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback; these assessments have a secondary formative purpose as well as their primary summative purpose. 8
 
0.1%
Other values (4276) 4810
89.8%
(Missing) 224
 
4.2%

Length

2023-05-11T21:36:30.441467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 42706
 
7.7%
and 24706
 
4.4%
to 22939
 
4.1%
of 21637
 
3.9%
in 11977
 
2.2%
a 11391
 
2.0%
will 9923
 
1.8%
students 9075
 
1.6%
their 7129
 
1.3%
be 6768
 
1.2%
Other values (9760) 388050
69.8%

Most occurring characters

ValueCountFrequency (%)
547711
14.9%
e 388754
 
10.6%
t 287060
 
7.8%
s 251045
 
6.8%
a 230499
 
6.3%
i 218759
 
5.9%
n 218333
 
5.9%
o 207407
 
5.6%
r 178423
 
4.9%
l 136771
 
3.7%
Other values (93) 1013536
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2966047
80.6%
Space Separator 547714
 
14.9%
Other Punctuation 49871
 
1.4%
Uppercase Letter 47802
 
1.3%
Control 31874
 
0.9%
Decimal Number 17020
 
0.5%
Dash Punctuation 6790
 
0.2%
Close Punctuation 4123
 
0.1%
Open Punctuation 3917
 
0.1%
Final Punctuation 2629
 
0.1%
Other values (4) 511
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 388754
13.1%
t 287060
 
9.7%
s 251045
 
8.5%
a 230499
 
7.8%
i 218759
 
7.4%
n 218333
 
7.4%
o 207407
 
7.0%
r 178423
 
6.0%
l 136771
 
4.6%
d 129183
 
4.4%
Other values (18) 719813
24.3%
Uppercase Letter
ValueCountFrequency (%)
T 13719
28.7%
S 5467
 
11.4%
A 3641
 
7.6%
E 3111
 
6.5%
I 3034
 
6.3%
P 2128
 
4.5%
C 2038
 
4.3%
F 1878
 
3.9%
M 1761
 
3.7%
O 1512
 
3.2%
Other values (16) 9513
19.9%
Other Punctuation
ValueCountFrequency (%)
. 23916
48.0%
, 17235
34.6%
: 1944
 
3.9%
; 1765
 
3.5%
/ 1501
 
3.0%
' 1244
 
2.5%
% 1158
 
2.3%
596
 
1.2%
* 198
 
0.4%
& 175
 
0.4%
Other values (6) 139
 
0.3%
Decimal Number
ValueCountFrequency (%)
0 4881
28.7%
1 4064
23.9%
2 3545
20.8%
5 1357
 
8.0%
3 1180
 
6.9%
4 929
 
5.5%
8 317
 
1.9%
6 309
 
1.8%
7 287
 
1.7%
9 151
 
0.9%
Math Symbol
ValueCountFrequency (%)
+ 24
38.1%
< 18
28.6%
= 17
27.0%
± 2
 
3.2%
~ 2
 
3.2%
Control
ValueCountFrequency (%)
15569
48.8%
15561
48.8%
744
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 6449
95.0%
331
 
4.9%
10
 
0.1%
Space Separator
ValueCountFrequency (%)
547711
> 99.9%
  3
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 4099
99.4%
] 24
 
0.6%
Open Punctuation
ValueCountFrequency (%)
( 3893
99.4%
[ 24
 
0.6%
Final Punctuation
ValueCountFrequency (%)
2496
94.9%
133
 
5.1%
Initial Punctuation
ValueCountFrequency (%)
312
70.0%
134
30.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Other Number
ValueCountFrequency (%)
¾ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3013849
81.9%
Common 664449
 
18.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 388754
12.9%
t 287060
 
9.5%
s 251045
 
8.3%
a 230499
 
7.6%
i 218759
 
7.3%
n 218333
 
7.2%
o 207407
 
6.9%
r 178423
 
5.9%
l 136771
 
4.5%
d 129183
 
4.3%
Other values (44) 767615
25.5%
Common
ValueCountFrequency (%)
547711
82.4%
. 23916
 
3.6%
, 17235
 
2.6%
15569
 
2.3%
15561
 
2.3%
- 6449
 
1.0%
0 4881
 
0.7%
) 4099
 
0.6%
1 4064
 
0.6%
( 3893
 
0.6%
Other values (39) 21071
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3674271
99.9%
Punctuation 4014
 
0.1%
None 13
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
547711
14.9%
e 388754
 
10.6%
t 287060
 
7.8%
s 251045
 
6.8%
a 230499
 
6.3%
i 218759
 
6.0%
n 218333
 
5.9%
o 207407
 
5.6%
r 178423
 
4.9%
l 136771
 
3.7%
Other values (80) 1009509
27.5%
Punctuation
ValueCountFrequency (%)
2496
62.2%
596
 
14.8%
331
 
8.2%
312
 
7.8%
134
 
3.3%
133
 
3.3%
10
 
0.2%
2
 
< 0.1%
None
ValueCountFrequency (%)
à 6
46.2%
  3
23.1%
± 2
 
15.4%
é 1
 
7.7%
¾ 1
 
7.7%

ExemptFromAssessment
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
False
4883 
True
 
476
ValueCountFrequency (%)
False 4883
91.1%
True 476
 
8.9%
2023-05-11T21:36:30.561589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

ExemptFromAssessmentDate
Categorical

HIGH CARDINALITY  HIGH CORRELATION  MISSING 

Distinct67
Distinct (%)14.0%
Missing4879
Missing (%)91.0%
Memory size42.0 KiB
16-05-2016
242 
07-03-2016
40 
10-03-2016
 
23
12-04-2016
 
15
22-02-2008
 
14
Other values (62)
146 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4800
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)7.9%

Sample

1st row10-03-2016
2nd row10-03-2016
3rd row10-03-2016
4th row10-03-2016
5th row09-03-2017

Common Values

ValueCountFrequency (%)
16-05-2016 242
 
4.5%
07-03-2016 40
 
0.7%
10-03-2016 23
 
0.4%
12-04-2016 15
 
0.3%
22-02-2008 14
 
0.3%
26-03-2009 11
 
0.2%
14-02-2008 8
 
0.1%
18-02-2008 8
 
0.1%
28-09-2010 7
 
0.1%
16-02-2009 6
 
0.1%
Other values (57) 106
 
2.0%
(Missing) 4879
91.0%

Length

2023-05-11T21:36:30.638672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
16-05-2016 242
50.4%
07-03-2016 40
 
8.3%
10-03-2016 23
 
4.8%
12-04-2016 15
 
3.1%
22-02-2008 14
 
2.9%
26-03-2009 11
 
2.3%
14-02-2008 8
 
1.7%
18-02-2008 8
 
1.7%
28-09-2010 7
 
1.5%
16-02-2009 6
 
1.2%
Other values (57) 106
22.1%

Most occurring characters

ValueCountFrequency (%)
0 1141
23.8%
- 960
20.0%
1 783
16.3%
2 678
14.1%
6 595
12.4%
5 270
 
5.6%
3 141
 
2.9%
8 77
 
1.6%
7 56
 
1.2%
9 50
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3840
80.0%
Dash Punctuation 960
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1141
29.7%
1 783
20.4%
2 678
17.7%
6 595
15.5%
5 270
 
7.0%
3 141
 
3.7%
8 77
 
2.0%
7 56
 
1.5%
9 50
 
1.3%
4 49
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 960
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1141
23.8%
- 960
20.0%
1 783
16.3%
2 678
14.1%
6 595
12.4%
5 270
 
5.6%
3 141
 
2.9%
8 77
 
1.6%
7 56
 
1.2%
9 50
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1141
23.8%
- 960
20.0%
1 783
16.3%
2 678
14.1%
6 595
12.4%
5 270
 
5.6%
3 141
 
2.9%
8 77
 
1.6%
7 56
 
1.2%
9 50
 
1.0%

ExemptFromAssessmentComment
Categorical

HIGH CARDINALITY  MISSING 

Distinct226
Distinct (%)70.6%
Missing5039
Missing (%)94.0%
Memory size42.0 KiB
The LLB degree programme is a qualifying law degree giving exemption from the first (academic) part of professional training for students wishing to become solicitors or barristers in England and Wales. The degree has a coherent, transparent and logical approach to assessment and has an assessment model tailored to the skills developed in the third year of the degree. In light of this FTLC (now FTLSEC) granted an exemption for third year LLB modules from the Faculty assessment tariff in May 2008, with the UG Dean commenting that Faculty felt that the model "was well reasoned and reflected a well thought through pattern of assessment".
32 
The extra half an hour exam duration allows students a little more time to reflect on their answers and to ensure that they have fully integrated the material that they have learned during the pre-school period, with any additional context and explanation gained during the intensive week.
 
8
The 2 hour theoretical exam needs to be this length to enable students to demonstrate the skills and learning attained through thie study of the preschool material.
 
7
It is recognised that the word count of 2,000 is below the Assessment Tariff upper limit of 4,000 words for a 20 credits module but it is acknowledged that these students will also be engaged in a full study workload at a partner institution if on a study placement or be in full –time employment if on a work placement. It is determined, therefore that 2,000 words is fair and will enable students to effectively demonstrate their reflective learning.
 
6
It is recognised that the word count of 3,000 is way out of line with Assessment Tariff for a 100 credits module but it should be acknowledged that these students will also be engaged in a full study workload at a partner institution if on a study placement or be in full –time employment if on a work placement. It is determined, therefore, that 3,000 words is fair when their placement workload is also taken into account.
 
6
Other values (221)
261 

Length

Max length1000
Median length483
Mean length322.90625
Min length2

Characters and Unicode

Total characters103330
Distinct characters85
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique193 ?
Unique (%)60.3%

Sample

1st rowExemption from Assessment Tariff granted in order to introduce an individual component to this module that has previously been assessed by means of 100% group work. the group report and a group presentation were deemed to be valuable pieces of assessment in the development of graduate attributes of stage 2 students and it was felt that both should be kept.
2nd rowWe ask to have two summative assessments: - A literature review of up to 2,000 words, and - A final project of up to 4,000 words. Such a possibility is explicitly mentioned in the University Assessment Guidelines.
3rd rowThis is a dissertation module and therefore requires exemption from standards assessment tariff restrictions.
4th row2 different themes are assessed separately, assessments test different Learning Outcomes
5th rowThese three distinctive L & T approaches are designed to maximise the transferable skill acquisition, both of this module, and therefore as a compulsory module, for an Agribusiness graduate. Each phase needs a summative assessment to ensure full engagement. Engagement in the team based learning is supported by the use of Buddycheck to moderate the portfolio mark to ensure a transparent reward mechanism for engagement. The essay has been changed from a summative component designed to offer feedback to a formative assessment thereby reducing the assessment tariff by one.

Common Values

ValueCountFrequency (%)
The LLB degree programme is a qualifying law degree giving exemption from the first (academic) part of professional training for students wishing to become solicitors or barristers in England and Wales. The degree has a coherent, transparent and logical approach to assessment and has an assessment model tailored to the skills developed in the third year of the degree. In light of this FTLC (now FTLSEC) granted an exemption for third year LLB modules from the Faculty assessment tariff in May 2008, with the UG Dean commenting that Faculty felt that the model "was well reasoned and reflected a well thought through pattern of assessment". 32
 
0.6%
The extra half an hour exam duration allows students a little more time to reflect on their answers and to ensure that they have fully integrated the material that they have learned during the pre-school period, with any additional context and explanation gained during the intensive week. 8
 
0.1%
The 2 hour theoretical exam needs to be this length to enable students to demonstrate the skills and learning attained through thie study of the preschool material. 7
 
0.1%
It is recognised that the word count of 2,000 is below the Assessment Tariff upper limit of 4,000 words for a 20 credits module but it is acknowledged that these students will also be engaged in a full study workload at a partner institution if on a study placement or be in full –time employment if on a work placement. It is determined, therefore that 2,000 words is fair and will enable students to effectively demonstrate their reflective learning. 6
 
0.1%
It is recognised that the word count of 3,000 is way out of line with Assessment Tariff for a 100 credits module but it should be acknowledged that these students will also be engaged in a full study workload at a partner institution if on a study placement or be in full –time employment if on a work placement. It is determined, therefore, that 3,000 words is fair when their placement workload is also taken into account. 6
 
0.1%
This exam involves a significant period of reading and reflection and the duration of the exam incorporates this. 6
 
0.1%
The 2 hour theoretical exam needs to be this length to enable students to demonstrate the skills and learning attained through the study of the preschool material. APPROVED: MA 5
 
0.1%
Re-approved by Simon Pallett 16.05.16. 4
 
0.1%
Unseen written exam 3
 
0.1%
This is a practical-based module undertaken in an industrial setting as part of a placement programme and as such does not lend itself to formative assessment. 3
 
0.1%
Other values (216) 240
 
4.5%
(Missing) 5039
94.0%

Length

2023-05-11T21:36:30.757781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 1206
 
7.3%
to 618
 
3.7%
of 569
 
3.4%
and 554
 
3.4%
a 408
 
2.5%
in 364
 
2.2%
assessment 350
 
2.1%
is 332
 
2.0%
for 263
 
1.6%
students 222
 
1.3%
Other values (1683) 11625
70.4%

Most occurring characters

ValueCountFrequency (%)
16194
15.7%
e 11039
 
10.7%
t 8133
 
7.9%
s 6838
 
6.6%
a 6485
 
6.3%
o 5782
 
5.6%
n 5726
 
5.5%
i 5543
 
5.4%
r 5202
 
5.0%
l 3535
 
3.4%
Other values (75) 28853
27.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82066
79.4%
Space Separator 16194
 
15.7%
Uppercase Letter 2117
 
2.0%
Other Punctuation 1337
 
1.3%
Decimal Number 975
 
0.9%
Control 191
 
0.2%
Open Punctuation 131
 
0.1%
Close Punctuation 131
 
0.1%
Dash Punctuation 129
 
0.1%
Final Punctuation 40
 
< 0.1%
Other values (2) 19
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11039
13.5%
t 8133
9.9%
s 6838
 
8.3%
a 6485
 
7.9%
o 5782
 
7.0%
n 5726
 
7.0%
i 5543
 
6.8%
r 5202
 
6.3%
l 3535
 
4.3%
d 3522
 
4.3%
Other values (16) 20261
24.7%
Uppercase Letter
ValueCountFrequency (%)
T 445
21.0%
L 237
11.2%
E 162
 
7.7%
F 162
 
7.7%
I 142
 
6.7%
S 139
 
6.6%
A 128
 
6.0%
C 107
 
5.1%
B 81
 
3.8%
M 79
 
3.7%
Other values (15) 435
20.5%
Other Punctuation
ValueCountFrequency (%)
. 658
49.2%
, 458
34.3%
" 70
 
5.2%
/ 45
 
3.4%
' 33
 
2.5%
% 27
 
2.0%
: 21
 
1.6%
; 16
 
1.2%
& 5
 
0.4%
* 3
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 422
43.3%
2 202
20.7%
1 129
 
13.2%
3 62
 
6.4%
8 52
 
5.3%
4 34
 
3.5%
5 33
 
3.4%
6 23
 
2.4%
7 10
 
1.0%
9 8
 
0.8%
Control
ValueCountFrequency (%)
93
48.7%
93
48.7%
5
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 114
88.4%
15
 
11.6%
Final Punctuation
ValueCountFrequency (%)
37
92.5%
3
 
7.5%
Initial Punctuation
ValueCountFrequency (%)
9
75.0%
3
 
25.0%
Space Separator
ValueCountFrequency (%)
16194
100.0%
Open Punctuation
ValueCountFrequency (%)
( 131
100.0%
Close Punctuation
ValueCountFrequency (%)
) 131
100.0%
Math Symbol
ValueCountFrequency (%)
+ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 84183
81.5%
Common 19147
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11039
13.1%
t 8133
 
9.7%
s 6838
 
8.1%
a 6485
 
7.7%
o 5782
 
6.9%
n 5726
 
6.8%
i 5543
 
6.6%
r 5202
 
6.2%
l 3535
 
4.2%
d 3522
 
4.2%
Other values (41) 22378
26.6%
Common
ValueCountFrequency (%)
16194
84.6%
. 658
 
3.4%
, 458
 
2.4%
0 422
 
2.2%
2 202
 
1.1%
( 131
 
0.7%
) 131
 
0.7%
1 129
 
0.7%
- 114
 
0.6%
93
 
0.5%
Other values (24) 615
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103263
99.9%
Punctuation 67
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16194
15.7%
e 11039
 
10.7%
t 8133
 
7.9%
s 6838
 
6.6%
a 6485
 
6.3%
o 5782
 
5.6%
n 5726
 
5.5%
i 5543
 
5.4%
r 5202
 
5.0%
l 3535
 
3.4%
Other values (70) 28786
27.9%
Punctuation
ValueCountFrequency (%)
37
55.2%
15
22.4%
9
 
13.4%
3
 
4.5%
3
 
4.5%

IsHepatitisAImmunisationOffered
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
False
5317 
True
 
42
ValueCountFrequency (%)
False 5317
99.2%
True 42
 
0.8%
2023-05-11T21:36:30.871876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

IsHepatitisBImmunisationOffered
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
False
5306 
True
 
53
ValueCountFrequency (%)
False 5306
99.0%
True 53
 
1.0%
2023-05-11T21:36:30.950947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

IsTetanusImmunisationOffered
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
False
5280 
True
 
79
ValueCountFrequency (%)
False 5280
98.5%
True 79
 
1.5%
2023-05-11T21:36:31.029018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
False
5344 
True
 
15
ValueCountFrequency (%)
False 5344
99.7%
True 15
 
0.3%
2023-05-11T21:36:31.107089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

GeneralNotes
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct289
Distinct (%)28.3%
Missing4339
Missing (%)81.0%
Memory size42.0 KiB
Original Handbook text:
677 
Students undertake a research project that is training for a future career as a professional physicist. The project is normally carried out in a research environment and is conducted by a member of staff with an expertise in the relevant academic field. Through working alongside a member of the research staff the student is introduced to research methodology.
 
7
Study Abroad outside the EU will require careful consideration of Visa and Health Insurance requirements pertaining to the country involved.
 
5
All staff periodically supervise MFA students
 
3
Students need to demonstrate Fitness to Practise in order to complete the clinical placement. Students need to pass all components of the module. See Policy on Failing Components of Clinical and Professional Education Modules & Procedure regarding Retrieval Placements.
 
3
Other values (284)
325 

Length

Max length3996
Median length23
Mean length141.41863
Min length3

Characters and Unicode

Total characters144247
Distinct characters94
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique247 ?
Unique (%)24.2%

Sample

1st rowOriginal Handbook text:
2nd rowOriginal Handbook text: ESSENTIAL TEXTBOOK Management and Cost Accounting (6th edition) - 5th edition also acceptable by Alnoor Bhimani, Charles T. Horngren, Srikant M. Datar, Madhav Rajan . Management and Cost Accounting (8th edition) - 7th edition also acceptable by Colin Drury
3rd rowOriginal Handbook text:
4th rowOriginal Handbook text:
5th rowOriginal Handbook text:

Common Values

ValueCountFrequency (%)
Original Handbook text: 677
 
12.6%
Students undertake a research project that is training for a future career as a professional physicist. The project is normally carried out in a research environment and is conducted by a member of staff with an expertise in the relevant academic field. Through working alongside a member of the research staff the student is introduced to research methodology. 7
 
0.1%
Study Abroad outside the EU will require careful consideration of Visa and Health Insurance requirements pertaining to the country involved. 5
 
0.1%
All staff periodically supervise MFA students 3
 
0.1%
Students need to demonstrate Fitness to Practise in order to complete the clinical placement. Students need to pass all components of the module. See Policy on Failing Components of Clinical and Professional Education Modules & Procedure regarding Retrieval Placements. 3
 
0.1%
N/a 3
 
0.1%
Students must apply to this study abroad year in accordance with the University application process for Study Abroad (either the Erasmus+ scheme or the Non-EU Exchange scheme). Students should inform the School of Biomedical Sciences of their intention to do this by the end of November of Stage 2. They will then be provided with appropriate advice and guidance during the application, preparation and pre-departure stage. This will include attendance of workshops and briefing sessions (e.g. from the International Office and Global Opportunities team). Students do not pay the full tuition fee for this year, but do pay the ‘assessed study abroad year’ fee currently in the order of £1,385 (existing fees for 2021-22). 3
 
0.1%
Music Technicians/ Technical Staff (Fred Hollingworth, Rob Blazey ) required for weekly student performances 3
 
0.1%
Students need to demonstrate Fitness to Practise in order to complete the clinical placement. Students need to pass all components of the module. See Policy on Failing Components of Clinical and Professional Education Modules & Procedure regarding Retrieval Placements 3
 
0.1%
Recommended to consider vaccination for Polio, Hepatitis A, Tetanus and Typhoid 2
 
< 0.1%
Other values (279) 311
 
5.8%
(Missing) 4339
81.0%

Length

2023-05-11T21:36:31.217188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 1140
 
5.3%
and 745
 
3.5%
handbook 733
 
3.4%
text 732
 
3.4%
original 729
 
3.4%
of 655
 
3.1%
to 589
 
2.7%
in 419
 
2.0%
a 414
 
1.9%
will 322
 
1.5%
Other values (3011) 14942
69.8%

Most occurring characters

ValueCountFrequency (%)
20184
14.0%
e 12992
 
9.0%
t 10227
 
7.1%
i 9201
 
6.4%
a 9139
 
6.3%
n 8808
 
6.1%
o 8800
 
6.1%
r 7301
 
5.1%
s 6537
 
4.5%
l 5281
 
3.7%
Other values (84) 45777
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 111254
77.1%
Space Separator 20184
 
14.0%
Uppercase Letter 5574
 
3.9%
Other Punctuation 3393
 
2.4%
Decimal Number 1650
 
1.1%
Control 1305
 
0.9%
Dash Punctuation 314
 
0.2%
Open Punctuation 228
 
0.2%
Close Punctuation 227
 
0.2%
Final Punctuation 64
 
< 0.1%
Other values (4) 54
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12992
11.7%
t 10227
 
9.2%
i 9201
 
8.3%
a 9139
 
8.2%
n 8808
 
7.9%
o 8800
 
7.9%
r 7301
 
6.6%
s 6537
 
5.9%
l 5281
 
4.7%
d 4913
 
4.4%
Other values (18) 28055
25.2%
Uppercase Letter
ValueCountFrequency (%)
H 817
14.7%
O 815
14.6%
S 491
 
8.8%
T 422
 
7.6%
C 396
 
7.1%
A 333
 
6.0%
P 289
 
5.2%
E 226
 
4.1%
I 200
 
3.6%
R 184
 
3.3%
Other values (16) 1401
25.1%
Other Punctuation
ValueCountFrequency (%)
. 1258
37.1%
: 919
27.1%
, 705
20.8%
/ 281
 
8.3%
; 109
 
3.2%
% 26
 
0.8%
& 24
 
0.7%
22
 
0.6%
' 18
 
0.5%
? 10
 
0.3%
Other values (4) 21
 
0.6%
Decimal Number
ValueCountFrequency (%)
2 369
22.4%
0 344
20.8%
1 321
19.5%
3 167
10.1%
4 105
 
6.4%
5 84
 
5.1%
9 80
 
4.8%
6 63
 
3.8%
7 61
 
3.7%
8 56
 
3.4%
Control
ValueCountFrequency (%)
633
48.5%
633
48.5%
39
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 309
98.4%
5
 
1.6%
Final Punctuation
ValueCountFrequency (%)
61
95.3%
3
 
4.7%
Initial Punctuation
ValueCountFrequency (%)
21
87.5%
3
 
12.5%
Math Symbol
ValueCountFrequency (%)
+ 6
85.7%
~ 1
 
14.3%
Space Separator
ValueCountFrequency (%)
20184
100.0%
Open Punctuation
ValueCountFrequency (%)
( 228
100.0%
Close Punctuation
ValueCountFrequency (%)
) 227
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 18
100.0%
Currency Symbol
ValueCountFrequency (%)
£ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 116828
81.0%
Common 27419
 
19.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12992
 
11.1%
t 10227
 
8.8%
i 9201
 
7.9%
a 9139
 
7.8%
n 8808
 
7.5%
o 8800
 
7.5%
r 7301
 
6.2%
s 6537
 
5.6%
l 5281
 
4.5%
d 4913
 
4.2%
Other values (44) 33629
28.8%
Common
ValueCountFrequency (%)
20184
73.6%
. 1258
 
4.6%
: 919
 
3.4%
, 705
 
2.6%
633
 
2.3%
633
 
2.3%
2 369
 
1.3%
0 344
 
1.3%
1 321
 
1.2%
- 309
 
1.1%
Other values (30) 1744
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 144124
99.9%
Punctuation 115
 
0.1%
None 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20184
14.0%
e 12992
 
9.0%
t 10227
 
7.1%
i 9201
 
6.4%
a 9139
 
6.3%
n 8808
 
6.1%
o 8800
 
6.1%
r 7301
 
5.1%
s 6537
 
4.5%
l 5281
 
3.7%
Other values (75) 45654
31.7%
Punctuation
ValueCountFrequency (%)
61
53.0%
22
 
19.1%
21
 
18.3%
5
 
4.3%
3
 
2.6%
3
 
2.6%
None
ValueCountFrequency (%)
£ 5
62.5%
ô 2
 
25.0%
è 1
 
12.5%

NonStandardSessionOfOffering_id
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5359
Missing (%)100.0%
Memory size42.0 KiB

AcademicYear
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
2022
5359 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters21436
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 5359
100.0%

Length

2023-05-11T21:36:31.333306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-11T21:36:31.417379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2022 5359
100.0%

Most occurring characters

ValueCountFrequency (%)
2 16077
75.0%
0 5359
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21436
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 16077
75.0%
0 5359
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21436
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 16077
75.0%
0 5359
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 16077
75.0%
0 5359
 
25.0%

AcademicYearId
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
22
5359 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10718
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22
2nd row22
3rd row22
4th row22
5th row22

Common Values

ValueCountFrequency (%)
22 5359
100.0%

Length

2023-05-11T21:36:31.488444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-11T21:36:31.572520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
22 5359
100.0%

Most occurring characters

ValueCountFrequency (%)
2 10718
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10718
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 10718
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10718
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 10718
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10718
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 10718
100.0%

SchoolCode
Categorical

Distinct30
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
D-SENG
714 
D-NUBS
671 
D-SACS
615 
D-SHIS
349 
D-SMLS
332 
Other values (25)
2678 

Length

Max length7
Median length6
Mean length5.9673447
Min length5

Characters and Unicode

Total characters31979
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowD-NUBS
2nd rowD-NUBS
3rd rowD-NUBS
4th rowD-NUBS
5th rowD-NUBS

Common Values

ValueCountFrequency (%)
D-SENG 714
13.3%
D-NUBS 671
12.5%
D-SACS 615
11.5%
D-SHIS 349
 
6.5%
D-SMLS 332
 
6.2%
D-SGPS 292
 
5.4%
D-MATH 277
 
5.2%
D-SNES 274
 
5.1%
D-ECLS 241
 
4.5%
D-COMP 212
 
4.0%
Other values (20) 1382
25.8%

Length

2023-05-11T21:36:31.652584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
d-seng 714
13.3%
d-nubs 671
12.5%
d-sacs 615
11.5%
d-shis 349
 
6.5%
d-smls 332
 
6.2%
d-sgps 292
 
5.4%
d-math 277
 
5.2%
d-snes 274
 
5.1%
d-ecls 241
 
4.5%
d-comp 212
 
4.0%
Other values (20) 1382
25.8%

Most occurring characters

ValueCountFrequency (%)
S 6945
21.7%
D 5400
16.9%
- 5359
16.8%
N 2090
 
6.5%
E 1520
 
4.8%
C 1246
 
3.9%
A 1225
 
3.8%
G 1153
 
3.6%
L 1112
 
3.5%
M 1021
 
3.2%
Other values (12) 4908
15.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 26620
83.2%
Dash Punctuation 5359
 
16.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 6945
26.1%
D 5400
20.3%
N 2090
 
7.9%
E 1520
 
5.7%
C 1246
 
4.7%
A 1225
 
4.6%
G 1153
 
4.3%
L 1112
 
4.2%
M 1021
 
3.8%
B 853
 
3.2%
Other values (11) 4055
15.2%
Dash Punctuation
ValueCountFrequency (%)
- 5359
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26620
83.2%
Common 5359
 
16.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 6945
26.1%
D 5400
20.3%
N 2090
 
7.9%
E 1520
 
5.7%
C 1246
 
4.7%
A 1225
 
4.6%
G 1153
 
4.3%
L 1112
 
4.2%
M 1021
 
3.8%
B 853
 
3.2%
Other values (11) 4055
15.2%
Common
ValueCountFrequency (%)
- 5359
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31979
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 6945
21.7%
D 5400
16.9%
- 5359
16.8%
N 2090
 
6.5%
E 1520
 
4.8%
C 1246
 
3.9%
A 1225
 
3.8%
G 1153
 
3.6%
L 1112
 
3.5%
M 1021
 
3.2%
Other values (12) 4908
15.3%

MarkingScale
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
M001
3172 
M003
2017 
M010
 
132
M009
 
29
MED1
 
8

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters21436
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowM001
2nd rowM001
3rd rowM001
4th rowM001
5th rowM001

Common Values

ValueCountFrequency (%)
M001 3172
59.2%
M003 2017
37.6%
M010 132
 
2.5%
M009 29
 
0.5%
MED1 8
 
0.1%
M007 1
 
< 0.1%

Length

2023-05-11T21:36:31.750674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-11T21:36:31.850764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
m001 3172
59.2%
m003 2017
37.6%
m010 132
 
2.5%
m009 29
 
0.5%
med1 8
 
0.1%
m007 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 10702
49.9%
M 5359
25.0%
1 3312
 
15.5%
3 2017
 
9.4%
9 29
 
0.1%
E 8
 
< 0.1%
D 8
 
< 0.1%
7 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16061
74.9%
Uppercase Letter 5375
 
25.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10702
66.6%
1 3312
 
20.6%
3 2017
 
12.6%
9 29
 
0.2%
7 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
M 5359
99.7%
E 8
 
0.1%
D 8
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16061
74.9%
Latin 5375
 
25.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10702
66.6%
1 3312
 
20.6%
3 2017
 
12.6%
9 29
 
0.2%
7 1
 
< 0.1%
Latin
ValueCountFrequency (%)
M 5359
99.7%
E 8
 
0.1%
D 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10702
49.9%
M 5359
25.0%
1 3312
 
15.5%
3 2017
 
9.4%
9 29
 
0.1%
E 8
 
< 0.1%
D 8
 
< 0.1%
7 1
 
< 0.1%

Module_Id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct5359
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107460.06
Minimum104529
Maximum117210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.0 KiB
2023-05-11T21:36:31.961864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum104529
5-th percentile104842.9
Q1105978.5
median107465
Q3108913.5
95-th percentile110026.1
Maximum117210
Range12681
Interquartile range (IQR)2935

Descriptive statistics

Standard deviation1707.9484
Coefficient of variation (CV)0.015893797
Kurtosis-0.76671981
Mean107460.06
Median Absolute Deviation (MAD)1468
Skewness0.10374814
Sum5.7587848 × 108
Variance2917087.7
MonotonicityStrictly increasing
2023-05-11T21:36:32.090982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104529 1
 
< 0.1%
108451 1
 
< 0.1%
108449 1
 
< 0.1%
108448 1
 
< 0.1%
108447 1
 
< 0.1%
108446 1
 
< 0.1%
108445 1
 
< 0.1%
108444 1
 
< 0.1%
108443 1
 
< 0.1%
108442 1
 
< 0.1%
Other values (5349) 5349
99.8%
ValueCountFrequency (%)
104529 1
< 0.1%
104530 1
< 0.1%
104531 1
< 0.1%
104532 1
< 0.1%
104533 1
< 0.1%
104534 1
< 0.1%
104535 1
< 0.1%
104536 1
< 0.1%
104537 1
< 0.1%
104538 1
< 0.1%
ValueCountFrequency (%)
117210 1
< 0.1%
117209 1
< 0.1%
111349 1
< 0.1%
111348 1
< 0.1%
111344 1
< 0.1%
111343 1
< 0.1%
111342 1
< 0.1%
111340 1
< 0.1%
111338 1
< 0.1%
111337 1
< 0.1%

Timestamp
Categorical

HIGH CARDINALITY  HIGH CORRELATION  IMBALANCE 

Distinct68
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
02-11-2021
4925 
27-01-2022
 
61
29-01-2022
 
52
09-02-2022
 
48
08-02-2022
 
35
Other values (63)
 
238

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters53590
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.5%

Sample

1st row02-11-2021
2nd row02-11-2021
3rd row02-11-2021
4th row02-11-2021
5th row14-01-2022

Common Values

ValueCountFrequency (%)
02-11-2021 4925
91.9%
27-01-2022 61
 
1.1%
29-01-2022 52
 
1.0%
09-02-2022 48
 
0.9%
08-02-2022 35
 
0.7%
01-02-2022 23
 
0.4%
25-03-2022 21
 
0.4%
01-03-2022 21
 
0.4%
03-02-2022 18
 
0.3%
11-02-2022 13
 
0.2%
Other values (58) 142
 
2.6%

Length

2023-05-11T21:36:32.201081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
02-11-2021 4925
91.9%
27-01-2022 61
 
1.1%
29-01-2022 52
 
1.0%
09-02-2022 48
 
0.9%
08-02-2022 35
 
0.7%
01-02-2022 23
 
0.4%
25-03-2022 21
 
0.4%
01-03-2022 21
 
0.4%
03-02-2022 18
 
0.3%
11-02-2022 13
 
0.2%
Other values (58) 142
 
2.6%

Most occurring characters

ValueCountFrequency (%)
2 16425
30.6%
1 15090
28.2%
0 10888
20.3%
- 10718
20.0%
9 127
 
0.2%
3 111
 
0.2%
7 79
 
0.1%
8 70
 
0.1%
5 35
 
0.1%
6 25
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 42872
80.0%
Dash Punctuation 10718
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 16425
38.3%
1 15090
35.2%
0 10888
25.4%
9 127
 
0.3%
3 111
 
0.3%
7 79
 
0.2%
8 70
 
0.2%
5 35
 
0.1%
6 25
 
0.1%
4 22
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 10718
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53590
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 16425
30.6%
1 15090
28.2%
0 10888
20.3%
- 10718
20.0%
9 127
 
0.2%
3 111
 
0.2%
7 79
 
0.1%
8 70
 
0.1%
5 35
 
0.1%
6 25
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53590
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 16425
30.6%
1 15090
28.2%
0 10888
20.3%
- 10718
20.0%
9 127
 
0.2%
3 111
 
0.2%
7 79
 
0.1%
8 70
 
0.1%
5 35
 
0.1%
6 25
 
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
False
5359 
ValueCountFrequency (%)
False 5359
100.0%
2023-05-11T21:36:32.287171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
False
5359 
ValueCountFrequency (%)
False 5359
100.0%
2023-05-11T21:36:32.359234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
False
5359 
ValueCountFrequency (%)
False 5359
100.0%
2023-05-11T21:36:32.430290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

IsSapUploadDisabled
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
False
5304 
True
 
55
ValueCountFrequency (%)
False 5304
99.0%
True 55
 
1.0%
2023-05-11T21:36:32.504357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

TeachingLocation
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
Newcastle City Campus
4817 
Off Campus
 
213
London
 
117
Mixed Location
 
100
Singapore
 
68

Length

Max length21
Median length21
Mean length19.84568
Min length6

Characters and Unicode

Total characters106353
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNewcastle City Campus
2nd rowNewcastle City Campus
3rd rowNewcastle City Campus
4th rowNewcastle City Campus
5th rowNewcastle City Campus

Common Values

ValueCountFrequency (%)
Newcastle City Campus 4817
89.9%
Off Campus 213
 
4.0%
London 117
 
2.2%
Mixed Location 100
 
1.9%
Singapore 68
 
1.3%
Malaysia 44
 
0.8%

Length

2023-05-11T21:36:32.587432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-11T21:36:32.693537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
campus 5030
32.9%
newcastle 4817
31.5%
city 4817
31.5%
off 213
 
1.4%
london 117
 
0.8%
mixed 100
 
0.7%
location 100
 
0.7%
singapore 68
 
0.4%
malaysia 44
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a 10147
 
9.5%
9947
 
9.4%
s 9891
 
9.3%
C 9847
 
9.3%
e 9802
 
9.2%
t 9734
 
9.2%
i 5129
 
4.8%
p 5098
 
4.8%
u 5030
 
4.7%
m 5030
 
4.7%
Other values (16) 26698
25.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 81100
76.3%
Uppercase Letter 15306
 
14.4%
Space Separator 9947
 
9.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10147
12.5%
s 9891
12.2%
e 9802
12.1%
t 9734
12.0%
i 5129
6.3%
p 5098
6.3%
u 5030
6.2%
m 5030
6.2%
c 4917
6.1%
l 4861
6.0%
Other values (9) 11461
14.1%
Uppercase Letter
ValueCountFrequency (%)
C 9847
64.3%
N 4817
31.5%
L 217
 
1.4%
O 213
 
1.4%
M 144
 
0.9%
S 68
 
0.4%
Space Separator
ValueCountFrequency (%)
9947
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 96406
90.6%
Common 9947
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10147
10.5%
s 9891
10.3%
C 9847
10.2%
e 9802
10.2%
t 9734
10.1%
i 5129
 
5.3%
p 5098
 
5.3%
u 5030
 
5.2%
m 5030
 
5.2%
c 4917
 
5.1%
Other values (15) 21781
22.6%
Common
ValueCountFrequency (%)
9947
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10147
 
9.5%
9947
 
9.4%
s 9891
 
9.3%
C 9847
 
9.3%
e 9802
 
9.2%
t 9734
 
9.2%
i 5129
 
4.8%
p 5098
 
4.8%
u 5030
 
4.7%
m 5030
 
4.7%
Other values (16) 26698
25.1%

Interactions

2023-05-11T21:36:16.944353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:10.598181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:11.583071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:12.424250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:13.312055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:14.321972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:15.234802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:16.082571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:17.054452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:10.830382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:11.685576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:12.533349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:13.419153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:14.436075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:15.340897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:16.188667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:17.161549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:10.932476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:11.786672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:12.641447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:13.526250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:14.550179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:15.442990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:16.293762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:17.277655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:11.044577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:11.894769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:12.756551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:13.639352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:14.672290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:15.551088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:16.407866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:17.392759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:11.155679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:12.005870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:12.871656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:13.755458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:14.788396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:15.660187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:16.526973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:17.508865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:11.268782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:12.114969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:12.985759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:13.873565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:14.906503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:15.767286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:16.637075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:17.613960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:11.369876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:12.214058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:13.090854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:13.981663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:15.011598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:15.864372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:16.735163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:17.722058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:11.474971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:12.315150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:13.198953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:14.091763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:15.118695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:15.972470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-11T21:36:16.836254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-05-11T21:36:32.819643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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IsHepatitisBImmunisationOffered0.0000.0000.0960.1490.0900.2150.0400.0940.1640.0380.0230.0240.1170.0080.0000.0140.0000.0000.0000.0540.1850.0160.0000.8141.0000.6210.4050.5470.2450.6290.0540.041
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Missing values

2023-05-11T21:36:18.097399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-11T21:36:19.180886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-11T21:36:21.135960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ModuleCodeSapObjectIdTitleShortTitleMaxCapacityIsNewIsDummySemester1OfferedSemester1CreditValueSemester2OfferedSemester2CreditValueSemester3OfferedSemester3CreditValueEctsCreditValueFheqLevelModeDeliveryStandAloneAvailabilityIsOfferedIsFtlcApprovedDateFtlcApprovedIsBosApprovedDateBosApprovedIsUploadedToSapDateSapUploadedPreRequisiteCommentCoRequisiteCommentAvailabilityAimsOutlineOfSyllabusStudyAbroadIntendedKnowledgeOutcomesIntendedSkillOutcomesGraduateSkillsFrameworkApplicableCriticalThinkingDataSynthesisActiveLearningNumeracyLiteracySelfAwarenessAndReflectionInnovationAndCreativityInitiativeIndependenceAdaptabilityProblemSolvingBudgetingOralForeignLanguagesInterpersonalWrittenOtherCollaborationRelationshipBuildingLeadershipNegotiationPeerAssessmentReviewOccupationalAwarenessMarketAwarenessGovernanceAwarenessFinancialAwarenessBusinessPlanningEthicalAwarenessSocialCulturalGlobalAwarenessLegalAwarenessSourceMaterialsSynthesiseAndPresentMaterialsUseOfComputerApplicationsGoalSettingAndActionPlanningDecisionMakingTeachingRationaleAndRelationshipAssessmentRationaleAndRelationshipExemptFromAssessmentExemptFromAssessmentDateExemptFromAssessmentCommentIsHepatitisAImmunisationOfferedIsHepatitisBImmunisationOfferedIsTetanusImmunisationOfferedIsAllergyScreeningOfferedGeneralNotesNonStandardSessionOfOffering_idAcademicYearAcademicYearIdSchoolCodeMarkingScaleModule_IdTimestampIsThemedAgeingIsThemedSocialRenewalIsThemedSustainabilityIsSapUploadDisabledTeachingLocation
0ACC200250340718Managerial and Business EconomicsManagerial and Business Economics999FalseFalseTrue10True10False0105LSTNTrueTrue02-02-2022True19-01-2022False14-02-2023NaNNoneETo introduce students to economic issues and decision-making tools, relating to major topics like demand analysis and estimation, production and cost functions, and decision making with differing market structures.OPTIMISATION: A BRIEF REVIEW\r\n\r\nCONSUMPTION & DEMAND\r\n•\tAxioms of traditional consumer theory\r\n• Quantitative demand analysis\r\n•\tIndifference curves, consumption decisions and demand\r\n\r\nPRODUCTION, COSTS & SUPPLY\r\n•\tEconomic analysis of production\r\n•\tCost functions\r\n• The organization of the firm\r\n\r\nMARKET STRUCTURE & PERFORMANCE OUTCOMES\r\n•\tMonopoly, price discrimination and welfare\r\n•\tMonopolistic competition\r\n•\tModels of Oligopoly: Cournot; Stackelberg; The kinked demand curve\r\n• Game theory\r\n\t\r\nMARKET STRUCTURE, FIRM STRATEGY & PERFORMANCE\r\n•\tEmpirical evidence on SCP paradigm and concentration - profitability\r\n•\tResource based view of the Firm\r\n•\tPersistance of profit\r\n\t\r\nANALYSIS OF FIRM STRATEGY\r\n•\tProduct differentiation\r\n•\tAdvertising\r\n•\tBarriers to entry\r\n\r\nTHEORIES OF THE FIRM\r\n•\tNeoclassical\r\n•\tBehavioural\r\n•\tTransaction-costs perspectivesYAt the end of this module students will be able to:\r\n•\tDemonstrate a conceptual understanding of business economics, and distinguish some difference from accounting concepts.\r\n•\tAnalyse the economic aspects of the nature of competition and behaviour of firms in an industry, and appraise their implications for the competitive strategies and performance of businesses.\r\n•\tApply economic techniques in managerial decision making, but also illustrate the limitations implicit in such techniques.At the end of this module students will be able to:\r\n•\tDevelop independent learning to prepare a written report.\r\n•\tDevelop quantitative skills to practice the using of statistical software, employ the appropriate models to analyse the data, and interpret the empirical findings.\r\n•\tAnalyse different market structures and equilibrium outcomes in each of them.FalseNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNFormal lecture materials are used to explain the issues, and to introduce concepts and techniques. Seminar sessions are held throughout the year, designed to be both explanatory and interactive and offering the opportunity to explore issues raised in lectures. Students have the opportunity to develop and practise key skills in these sessions. Students are able to judge their progress in the module through these sessions.Formal examination tests the students' intended knowledge outcomes and their ability to write about specific models/issues and solve numerical problems. The 1200 word report provides an opportunity for students to demonstrate their business knowledge and written communication (report writing) skills.\r\n\r\nIn the case of an alternative semester 2 assessment (worth 75% of the overall module mark) being necessary due to circumstances, the module leader will in the first instance consult with the DPD as to the requirements of the professional accrediting body to discuss possible acceptable alternatives. In 2020/21 this alternative was a 24 hour take home exam delivered online, and it is envisioned that if circumstances do not allow a present-in-person timed exam at the end of semester 2, and the professional body agrees, than this may well be an example of the type of alternative assessment which could be put in place.FalseNaNNaNFalseFalseFalseFalseOriginal Handbook text:NaN202222D-NUBSM00110452902-11-2021FalseFalseFalseFalseNewcastle City Campus
1ACC200350340719Financial ControlFinancial Control999FalseFalseTrue10True10False0105LSTNTrueTrue02-02-2022True19-01-2022False14-02-2023NaNNonE(a) To provide a framework of the methods and techniques of management accounting and control.\r\n\r\n(b) To provide a framework for an understanding of the design and operation of management accounting and control systems by considering conceptual and practical issues involved.The module is delivered by means of a series of lectures and seminars.\r\n\r\nIt covers:\r\nResearch methodologies of management accounting \r\nCosting (including ABC)\r\nPlanning and control systems (including budgeting and beyond budgeting)\r\nPerformance management system\r\nCurrent issues and transfer pricingYBy the end of the module students will be able to : \r\n\r\n- Demonstrate an understanding of the issues involved in designing and operating management accounting and control systems that require to serve diverse purposes. \r\n\r\n- Examine current research issues in management accounting.By the end of the module students will be able to:\r\n\r\n- Manipulate data into relevant management accounting and control information for planning, decision-making and control \r\n\r\n- Interpret management accounting and control informationFalseNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNLectures materials are used to explain relevant issues and to introduce appropriate concepts and techniques. Seminars are used to enable students to apply and develop skills in an interactive environment.The formal examination tests students' intended knowledge and skills outcomes, in particular the framing of data into relevant management accounting and control information and the use and interpretation of this information. The assessment scheme examines students on set problems of management accounting and control systems as applied to planning, decision-making and control.\r\n\r\nThe group project tests students on the application of management accounting and control systems by use of an extended problem/short case incorporating features of real-world complexity. Self and peer review will take place after the report, and individuals will receive the group mark adjusted according to self and peer review i.e. their own and their team members’ assessment of each other’s contributions to report. Each team is to keep a log of its meetings, which should be handed in with the report itself. The module leader retains the right to adjust individual marks where it is deemed necessary in the interests of fairness.FalseNaNNaNFalseFalseFalseFalseOriginal Handbook text:\r\n\r\nESSENTIAL TEXTBOOK\r\nManagement and Cost Accounting (6th edition)\r\n- 5th edition also acceptable\tby Alnoor Bhimani, Charles T. Horngren, Srikant M. Datar, Madhav Rajan \r\n. \t\t\t\t\t\r\nManagement and Cost Accounting (8th edition)\r\n- 7th edition also acceptable\tby Colin DruryNaN202222D-NUBSM00110453002-11-2021FalseFalseFalseFalseNewcastle City Campus
2ACC200550340720Intermediate Financial AccountingIntermediate Financial Accounting999FalseFalseTrue10True10False0105LSTNTrueTrue02-02-2022True19-01-2022False14-02-2023NaNNoneE1. To examine current financial reporting practice and how it impacts upon companies\r\n2. To enable students to develop accounts preparation and interpretation skills\r\n3. To provide an introduction to group accounts preparation\r\n\r\nThis is an intermediate financial reporting module. Most of the examples relate to companies reporting to shareholders. We shall consider how to account for a range of situations, including accounting for provisions, contingent liabilities and contingent assets, financial instruments and consolidation of group companies with reference to international accounting standards. The module will provide an introduction to the regulatory and IASB frameworks governing the production of financial statements.1. Company Financial Statements\r\n\r\n2. The Frameworks of Financial Reporting\t\t\r\n\r\n3. Preparation of consolidated Group AccountsCBy the end of the module, students should be able to:\r\nApply the requirements of Company Law and International Financial Reporting Standards concerning the format and content of company financial statements\r\nAssess and compare the effects of accounting policy choices on reported income, net assets and capitalBy the end of the module, students should be able to:\r\nPrepare and interpret published financial statements of limited companies in accordance with IFRSFalseNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNLecture material introduces the course material to students, and concentrates upon some of the more challenging aspects of financial reporting. Students are given a programme of required reading to supplement the lecture materials and are encouraged to attempt questions as well as reading around subjects as widely as possible. The synchronous sessions consolidate the course material by allowing students to tackle problems in a small group environment, where the seminar leader is available to provide explanations and give extra help as required. The practice questions set will help students to develop problem-solving, numeracy and written-communication skills. The synchronous sessions are designed to encourage discussion by probing the implications of alternative accounting policy choices and trends in financial reporting. Students are required to attend synchronous seminar sessions with their attempts at set questions ready to discuss these with the rest of the group.The semester 2 written examination tests the students' intended knowledge and skills outcomes, in particular their ability to write succinct essays and solve numerical problems, covering content from both semesters. The semester 1 MCQ examination will provide an assessment of students' core knowledge of IFRS standards covered in semester 1.\r\n\r\nIn the case of an alternative semester 2 assessment (worth 75% of the overall module mark) being necessary due to circumstances, the module leader will in the first instance consult with the DPD as to the requirements of the professional accrediting body to discuss possible acceptable alternatives. In 2020/21 this alternative was a 24 hour take home exam delivered online, and it is envisioned that if circumstances do not allow a present-in-person timed exam at the end of semester 2, and the professional body agrees, than this may well be an example of the type of alternative assessment which could be put in place.FalseNaNNaNFalseFalseFalseFalseOriginal Handbook text:NaN202222D-NUBSM00110453102-11-2021FalseFalseFalseFalseNewcastle City Campus
3ACC200750340721Responsible Corporate FinanceResponsible Corporate Finance999FalseFalseTrue10True10False0105LSTNTrueTrue02-02-2022True21-09-2022False14-02-2023Equivalent Stage 1 Maths Statistics moduleNoneEThis module aims for develop an understanding of responsible corporate finance by examining analytical frameworks for the knowledge of the firm's major financing decisions by considering the theoretical models that explain these decisions. The module also examines Finance and Professional Codes of Conduct & Ethics, using real world examples to highlight professional behaviours and the changing ESG environment that accountants and treasurers are now expected to operate in.1- Scope and nature of corporate finance\r\n2- Valuation of debt and equity\r\n3- Security and portfolio analysis \r\n4- Capital market efficiency\r\n5- Asset pricing models and their applications\r\n\r\n6- Raising Capital: Debt and Equity\r\n7- Risk Management: Options, FRA's and Futures\r\n8- Capital structure\r\n9- Dividend policy\r\n10- Finance, ESG and Professional Codes of Conduct & EthicsYBy the end of the module students will be able to \t\r\n- Demonstrate critical understanding of theories and models in finance, and the way they are developed.\r\n- Evaluate the key financial decisions faced by a firm and how theories can inform practice.\r\n- Compare different approaches to solve financial problems and the ability to critically evaluate them under different circumstances.\r\n- Analyse, summarize and interpret academic research for financial decision making.By the end of the module students will acquire the following skills:\r\n- Quantitative skills in financial analysis and projections \r\n- Critical discussion with evidence\r\n- Effective communication\r\n- Solve structured and unstructured problemsFalseNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNLecture materials are designed to provide an introduction and exposition of key models, research and financial decisions\r\n\r\nPrivate study enables students to develop this in more detail.\r\n\r\nScehduled contact time provides an opportunity for students to work individually and in groups to discuss reading and work through problem questions. Group sessions provide an opportunity for students to develop their problem solving skillsThe formal examination assesses the students' intended knowledge and skills outcomes as well as problem solving, numeracy and written communication skills.\r\n\r\nFor 2022/23 onwards it is the intention of the module team to move the Inspera digital exams platform for an in-person digital 3 hour closed book exam. Should this not be possible, the exam will remain as a written 3 hour closed book exam.\r\n\r\nIn the case of an alternative semester 2 assessment being necessary due to circumstances, the module leader will in the first instance consult with the DPD as to the requirements of the professional accrediting body to discuss possible acceptable alternatives. In 2020/21 this alternative was a 24 hour take home exam delivered online, and it is envisioned that if circumstances do not allow a present-in-person timed exam (digital or written) at the end of semester 2, and the professional body agrees, than this may well be an example of the type of alternative assessment which could be put in place.FalseNaNNaNFalseFalseFalseFalseOriginal Handbook text:NaN202222D-NUBSM00110453202-11-2021FalseFalseFalseFalseNewcastle City Campus
4ACC200850604711Introduction to Corporate FinanceIntroduction to Corporate Finance999FalseFalseTrue10False0False055LSTNTrueTrue02-02-2022True13-01-2022False14-02-2023Sufficient Knowledge of Maths and Stats and Introductory Finance.NoneETo provide an analytical framework for the knowledge of the firm's major financing decisions by considering the theoretical models that explain these decisions.1- Scope and nature of corporate finance\r\n2- Valuation of debt and equity\r\n3- Security and portfolio analysis \r\n4- Capital market efficiency\r\n5- Asset pricing models and their applicationsYBy the end of the module students will be able to \t\r\n- Demonstrate critical understanding of theories and models in finance, and the way they are developed.\r\n- Evaluate the key financial decisions faced by a firm and how theories can inform practice.\r\n- Compare different approaches to solve financial problems and the ability to critically evaluate them under different circumstances.\r\n- Analyse, summarize and interpret academic research for financial decision making.1. Financial Analysis\r\n2. Critical evaluation of arguments and evidence\r\n3. Drawing conclusions from problemsFalseNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNLectures are designed to provide an introduction and exposition of key models, research and financial decisions [A1]\r\nPrivate study enables students to develop this in more detail.\r\n\r\nSeminars provide an opportunity for students to work individually and in groups to discuss reading and work through problem questions. [B1-B3]. Group feedback sessions provide an opportunity for students to develop their problem solving skills [B1] - [B3]The examination assesses the students' intended knowledge and skills outcomes as well as problem solving and numeracy skills.FalseNaNNaNFalseFalseFalseFalseOriginal Handbook text:NaN202222D-NUBSM00110453314-01-2022FalseFalseFalseFalseNewcastle City Campus
5ACC200950830516Strategic Business AnalysisStrategic Business Analysis999FalseFalseTrue10True10False0105LSTNTrueTrue02-02-2022True13-01-2022False14-02-2023ECO1017, or equivalent modulesNaNETo enable students to understand how businesses develop and implement strategic choices.\r\n\r\nTo understand and analyse business objectives, market position, and internal and external factors affecting strategic choices.Analysis of the internal and external strategic position of organisations, both international and domestic.\r\n\r\nIdentification, justification and selection of appropriate strategic options for an organisation. \r\n\r\nImplementation of strategic options, including marketing, human resource management and the effect of changing technology.YBy the end of the module students will be able to:\r\n- analyse business objectives, market position and strategic direction\t\t\r\n\t\t\r\n- examine the likely consequences of strategic choices and question appropriate strategies\t\t\r\n\t\t\r\n - use relevant models and techniques to aid practical implementation of strategyBy the end of the module students will be able to:\r\n\r\n- analyse current strategic issues in business\t\t\r\n\t\t\r\n- apply relevant tools and techniques to debate the effect of strategic issues on business\t\t\r\n\t\t\r\n- criticise theoretical models and question their usefulness in the current business environmentFalseNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNThis module aims to introduce students to some key concepts relating to business strategy. Lecture materials are used to explain the issues, and to introduce theory, concepts and techniques. Practice questions and discussion boards will allow for discussion and application of the material covered and issues raised. Students will have the opportunity to develop their understanding and judge their progress in these sessions. Reading is required to support the module and is essential to developing a rounded view of the material.Formative assessment will be used to prepare students for the final examination which needs to be in a particular format to meet accreditation requirements.\r\nFormative work throughout the course will be used to prepare students for the final examination which needs to be in a particular format to meet accreditation requirements. This work is not assessed.\r\n\r\nIn the case of an alternative semester 2 assessment being necessary due to circumstances, the module leader will in the first instance consult with the DPD as to the requirements of the professional accrediting body to discuss possible acceptable alternatives. In 2020/21 this alternative was a 24 hour take home exam delivered online, and it is envisioned that if circumstances do not allow a present-in-person timed exam at the end of semester 2, and the professional body agrees, than this may well be an example of the type of alternative assessment which could be put in place.FalseNaNNaNFalseFalseFalseFalseNaNNaN202222D-NUBSM00110453402-11-2021FalseFalseFalseFalseNewcastle City Campus
6ACC202050830520AuditingAuditing999FalseFalseTrue10True10False0105LSTNTrueTrue02-02-2022True13-01-2022True14-02-2023NoneNaNETo enable students to understand what auditing means in the context of professional frameworks: how organisations try to seek audit assurance and how this is reported to the relevant stakeholders within an international context.Planning and risk assessment\r\nInternal control\t\r\nInternal audit and corporate governance\r\nGathering audit evidence; assessing and using Internal controls\r\nReview and reporting\r\nProfessional ethicsCAt the end of the module students will be able in relation to:\r\n•\tStatutory Audit and Other Assurance – discuss the objectives and principal characteristics of statutory audit and other assurance engagements. \r\n•\tUnderstanding the business - evaluate information systems, internal control systems and identify significant business risks (financial, operational and compliance). \r\n•\tAudit Components – demonstrate an understanding of the issues involved in planning an audit and in obtaining, recording and evaluating audit evidence.\r\n•\tReporting and Communicating – discuss the nature, content and timing of the various forms of report through which the auditor communicates, including the ability to communicate the outcomes of statutory audits and other assurance engagements clearly, concisely and unambiguously.At the end of this module students will be able to:\r\n•\tDemonstrate knowledge and understanding of ethical, technical, legal and professional aspects of audit and assurance.\r\n•\tDiscuss and apply concepts which impact on the audit and assurance process.\r\n•\tInterpret and assess the impact of data, evidence and issues on the audit and assurance process.\r\n•\tDevelop problem solving skills in diverse scenario-based socio-cultural environments.FalseNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNThe syllabus requires students to master a wide range of principles and concepts, and then to apply to given scenarios. Helping students acquire this competence is best achieved by lecture materials, supported by seminars, significant reading and question practice as guided independent study.Unseen examination is required to match professional recognition requirements. It is also an appropriate way of assessing knowledge and application of the principles and concepts involved.\r\nStudents will receive feedback on their progress through undertaking worked examples and questions in class and during seminars.\r\n\r\nIn the case of an alternative semester 2 assessment (worth 75% of the overall module mark) being necessary due to circumstances, the module leader will in the first instance consult with the DPD as to the requirements of the professional accrediting body to discuss possible acceptable alternatives.FalseNaNNaNFalseFalseFalseFalseOriginal Handbook text:NaN202222D-NUBSM00110453502-11-2021FalseFalseFalseFalseNewcastle City Campus
7ACC202150340733Understanding Company AccountsUnderstanding Company Accounts999FalseFalseTrue10True10False0105LSTNTrueTrue02-02-2022True13-01-2022True14-02-2023An understanding of financial Accounting is required for the module provided by ACC1010NoneE- to enable students to appraise a company's financial condition and performance from its published accounts, paying particular attention to the context of its operating environment and accounting policy choices\r\n\r\n- To develop project management skills\r\n\r\n- To develop team working skills\r\n\r\n- To develop report writing skills\r\n\r\n- To develop presentational skills\r\n\r\n- To develop computer literacyAnalysis of the business environment, ratio analysis, report writing, working in teams, work on presentations.\r\n\r\nA 6000 word project and presentation will be carried out in teams of students, analysing a set of company accounts.CBy the end of the module students should be able to:\r\nExamine the key indicators of financial performance and compare the performance of one company with others. \r\nAppraise the impact of accounting policies on financial disclosures\r\nInvestigate the interaction of a company with the business environment to appreciate that accounting does not exist in a vacuum. \r\nUse appropriate software to process financial information and rationalise and present the results.By the end of the module students should be able to:\r\nPresent an analysis of financial statements, including ratio analysis, that illustrates and compares (by means of graphs, tables and charts as appropriate) trends and features of the financial performance and position of a publicly listed company and, where appropriate comparative companies. \r\nAnalyse the operating environment and history of a publicly listed company\r\nCarry out self reflection on their experience and development in the module and present this in a multimedia format.FalseNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNLectures materials and module talks will explain the aims, objectives, structure and operation of the module, students will be placed in teams and a company will be allocated to each team. Lecture material and learning and teaching on team working, self reflection and on the analysis and interpretation of company accounts will provide a framework for understanding the following key indicators of financial performance:\r\n1. level of growth\r\n2. profitability, cost structure and impact of accounting policies;\r\n3. cash generation and liquidity;\r\n4. level of investment as a safeguard for future profitability;\r\n5. structure of a company's finance.\r\n\r\nStudents are expected to obtain relevant background information for example via the the library, which is well-stocked with periodicals and other relevant sources e.g. Financial Times, industry/economic reports etc. All students have the opportunity to consult with academic staff on a group basis throughout the module. Students are required to take the initiative to arrange team meetings, consultations with staff as required, and to play a full part in the work of the group. Team working, research and report writing skills are thus developed through the project as well as analytical and numerical skills.Self and peer review will take place after the report and presentations, and individuals will receive the group mark adjusted according to self and peer review i.e. their own and their team members' assessment of each other's contributions to report and presentation. Each team is to keep a log of its meetings, which should be handed in with the project itself. The Module Leader retains the right to adjust individual marks where it is deemed necessary in the interests of fairness.\r\nNon-engagement with the team and module materials may result in a student being considered as achieving 0 marks for the group part of the module. \r\n\r\nThe group report, group presentation and multimedia self reflection test the students' knowledge and skills outcomes without exception.True10-03-2016Exemption from Assessment Tariff granted in order to introduce an individual component to this module that has previously been assessed by means of 100% group work. \r\nthe group report and a group presentation were deemed to be valuable pieces of assessment in the development of graduate attributes of stage 2 students and it was felt that both should be kept.FalseFalseFalseFalseOriginal Handbook text:NaN202222D-NUBSM00110453602-11-2021FalseFalseFalseFalseNewcastle City Campus
8ACC202450607836Management Accounting SystemsManagement Accounting Systems999FalseFalseTrue10False0False055LSTNTrueTrue02-02-2022True24-08-2022True20-10-2021Introductory Management AccountingNoneE(a) To provide a framework of the methods and techniques of management accounting and control.\r\n\r\n(b) To provide a framework for an understanding of the design and operation of management accounting and control systems by considering conceptual and practical issues involved.The module is delivered by means of a series of lectures and seminars.\r\n\r\nIt covers:\r\nResearch methodologies of management accounting \r\nCosting (including ABC)\r\nPlanning and control systems (including budgeting and beyond budgeting)\r\nPerformance management system\r\nCurrent issues and transfer pricingYBy the end of the module students will be able to : \r\n\r\n- Demonstrate an understanding of the issues involved in designing and operating management accounting and control systems that require to serve diverse purposes. \r\n\r\n- Examine current research issues in management accounting.By the end of the module students will be able to:\r\n\r\n- Manipulate data into relevant management accounting and control information for planning, decision-making and control \r\n\r\n- Interpret management accounting and control informationFalseNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNLectures are used to explore relevant issues and to introduce appropriate concepts and techniques. Seminars are used to enable students to apply and develop skills in an interactive environment.Assignment-short case study requiring numerical analysis and the production of a short written report (c. 500 words)\r\n\r\nThe assessment is designed to test all the learning outcomes.\r\n\r\nSEMESTER ONE ONLY STUDY ABROAD: assessment remains unchanged but submission date earlierFalseNaNNaNFalseFalseFalseFalseOriginal Handbook text:NaN202222D-NUBSM00110453702-11-2021FalseFalseFalseFalseNewcastle City Campus
9ACC202550608953SA Sem 1 Intermediate Financial AccountingSA Sem 1 Intermediate Financial Accnt999FalseFalseTrue10False0False055LSTNTrueTrue02-02-2022True13-01-2022False14-02-2023Prior accounting study essentialNoneETo examine current financial reporting practice and how it impacts on companies. \r\nTo develop accounts preparation skills.Preparation and presentation of published financial statements of limited companies in accordance with selected International Accounting Standards.YBy the end of the module, students should be able to:\r\nApply the requirements of company law and International Financial Reporting Standards concerning the format and content of company financial statements. \r\nAssess and compare the effects of accounting policy choice on reported income, net assets and capitalBy the end of the module, students should be able to:\r\nPrepare published financial statements of limited companies in accordance with IFRSFalseNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNLectures introduce the course material and concentrate on expositions of some of the more complex aspects. Students are given a programme of reading to support the lecture. Seminars consolidate course material by allowing students to tackle problems in an environment where they can seek help and explanations, as well as getting feedback on how well they are progressing. Seminars also help develop key skills in problem-solving and numeracy. Students are required to come to sessions with their attempts at set questions and then to discuss these issues in small teams.100% in class test.FalseNaNNaNFalseFalseFalseFalseOriginal Handbook text:NaN202222D-NUBSM00110453802-11-2021FalseFalseFalseFalseNewcastle City Campus
ModuleCodeSapObjectIdTitleShortTitleMaxCapacityIsNewIsDummySemester1OfferedSemester1CreditValueSemester2OfferedSemester2CreditValueSemester3OfferedSemester3CreditValueEctsCreditValueFheqLevelModeDeliveryStandAloneAvailabilityIsOfferedIsFtlcApprovedDateFtlcApprovedIsBosApprovedDateBosApprovedIsUploadedToSapDateSapUploadedPreRequisiteCommentCoRequisiteCommentAvailabilityAimsOutlineOfSyllabusStudyAbroadIntendedKnowledgeOutcomesIntendedSkillOutcomesGraduateSkillsFrameworkApplicableCriticalThinkingDataSynthesisActiveLearningNumeracyLiteracySelfAwarenessAndReflectionInnovationAndCreativityInitiativeIndependenceAdaptabilityProblemSolvingBudgetingOralForeignLanguagesInterpersonalWrittenOtherCollaborationRelationshipBuildingLeadershipNegotiationPeerAssessmentReviewOccupationalAwarenessMarketAwarenessGovernanceAwarenessFinancialAwarenessBusinessPlanningEthicalAwarenessSocialCulturalGlobalAwarenessLegalAwarenessSourceMaterialsSynthesiseAndPresentMaterialsUseOfComputerApplicationsGoalSettingAndActionPlanningDecisionMakingTeachingRationaleAndRelationshipAssessmentRationaleAndRelationshipExemptFromAssessmentExemptFromAssessmentDateExemptFromAssessmentCommentIsHepatitisAImmunisationOfferedIsHepatitisBImmunisationOfferedIsTetanusImmunisationOfferedIsAllergyScreeningOfferedGeneralNotesNonStandardSessionOfOffering_idAcademicYearAcademicYearIdSchoolCodeMarkingScaleModule_IdTimestampIsThemedAgeingIsThemedSocialRenewalIsThemedSustainabilityIsSapUploadDisabledTeachingLocation
5349CSC843353395824Business Applications 1Business Applications 1999TrueFalseFalse0False0True1057LBLNTrueTrue27-09-2022True27-09-2022True27-09-2022NaNNaNEThis module aims to equip apprentices with the technical and practical experience to function as successful software engineering practitioners. It will provide apprentices with a Project Based challenge* that requires them to learn and apply their skills in Advanced Programming and Web Technologies, previously learned skills in Human-Computer-Interaction, Project Management and Software Engineering processes.\r\n\r\nIn this module, the apprentices will design, develop, and manage a software engineering project and its timeline following agile, lean or other industry recognized approaches. They will apply HCI methodologies to design a product driven by user experience that incorporates an extended knowledge of Java. Further, they will create a presentation based on their product for stakeholders. \r\n\r\n*Apprentices and their employers who wish to apply a project from their workplace must consult with the Module Leader to ensure the scope is manageable in the semester, and the project criteria are met. Please note that this Module focuses on group work. Employers must be able to give the Apprentice a project that includes group work throughout.The module will cover:\r\n•Advanced Programming:\r\no\tServer side programming\r\no\tCommon data structures and manipulation of collections of objects.\r\no\tConcurrent programming\r\n•Introduction to Database Systems\r\n•Web Technologies:\r\no\tBasic languages (e.g. HTML, CSS, JavaScript).\r\no\tForms & scripts.\r\no\tClient-server development.\r\no\tWeb/Database integration.NTo be able to\r\no\t(K2) Describe & discuss advanced features of Java programmingincluding: o Concurrent programming,\r\no\tServer programming\r\n•\t(K1)Describe current and emerging Internet technologies\r\n•\t(K1, K2)Explain the relevant technology underlying web content delivery and presentation\r\n•\t(K2, K4)Describe database system design and use\r\n•\t\r\nDegree Apprenticeship Standards\r\nThe outcomes listed above support the Digital and Technology Solutions Specialist; Software Engineering Specialist, Level 7 domain specific outcomes:\r\n•\tKnowledge 1: The rationale for software platform and solution development, including the organisational context.\r\n•\tKnowledge 2: The various inputs, statements of requirements, security considerations and constraints that guide solution architecture and the development of logical and physical systems' designs;\r\n•\tKnowledge 4: The approaches used to modularise the internal structure of an application and describe the structure and behaviour of applications used in a business, with a focus on how they interact with each other and with business users;\r\n\r\nImportant to note that should MoF outcomes be adjusted they directly impact the credibility of the Apprenticeships Domain standards. The Domain standards are also listed in the End Point Assessment (EPA), Capstone MOF, and need to be introduced early in the programme to help apprentices develop and practice these skills.Intended Skills Outcomes:\r\nTo be able to\r\n•\tUse advanced features of Java programs including (S3):\r\no\tConcurrent and event-driven programming,\r\no\tSimple algorithms and data structures.\r\n•\t\r\n•\tIdentify the principal hardware components and software services which provide infrastructure for the Internet from the global scale down to the desktop (S3).\r\n•\t\r\n•\tConstruct simple web-based applications using common, current tools and systems.(S3) (C PC S7)\r\n•\t\r\n•\tIdentify, document, review and design IT enabled business processes that define a set of activities that will accomplish specific organisational goals and provides a systematic approach to improving processes (S6) (C PC S7)\r\n\r\nIntended Behaviour Outcomes:\r\nTo be able to\r\n•\tInspire and motivate others to deliver excellent technical solutions and outcomes (C L B1)\r\n•\tEstablish high levels of performance in digital and technology solutions activities (C L B2)\r\n•\tCreate strong positive relationship with team members to produce high performing technical teams (C L B3)\r\n•\tBe results and outcomes driven to achieve high key performance outcomes for digital and technology solutions objectives (C L B4)\r\n•\tPromote a high level of cooperation between own work group and other groups to establish a technology change led culture (C L B5)\r\n•\tDevelop and support others in developing an appropriate balance of leadership and technical skills (C L B6)\r\n\r\nDegree Apprenticeship Standards\r\nThe outcomes listed above support the Digital and Technology Solutions Specialist; Software Engineering Specialist, Level 7 domain specific outcomes:\r\n•\tSkill 3: Develop and deliver, distributed or semi-complex software solutions that are scalable and which deliver innovative user experiences and journeys that encompass cross-functional teams, platforms and technologies\r\n•\tSkill 6: Be accountable for the quality of deliverables from one or more software development teams (source code quality, automated testing, design quality, documentation etc.) and following company standard processes (code reviews, unit testing, source code management etc.).\r\n\r\nThis MoF also aligns with the Core standards (C)\r\n•\tLeadership Behaviour 1: Inspire and motivate others to deliver excellent technical solutions and outcomes (C L B1)\r\n•\tLeadership Behaviour 2: Establish high levels of performance in digital and technology solutions activities (C L B2)\r\n•\tLeadership Behaviour 3: Be results and outcomes driven to achieve high key performance outcomes for digital and technology solutions objectives (C L B3)\r\n•\tLeadership Behaviour 4: Promote a high level of cooperation between own work group and other groups to establish a technology change led culture. (C L B4)\r\n•\tLeadership Behaviour 5: Develop and support others in developing an appropriate balance of leadership and technical skills (C L B5)\r\n•\tLeadership Behaviour 6: Create strong positive relationship with team members to produce high performing technical teams (C L B6)\r\n•\tProfessional Competencies Skill 7: Demonstrate self-direction and originality in solving problems, and act autonomously in planning and implementing digital and technology solutions specialist tasks at a professional level (C PC S7)\r\n•\tBusiness and Change Management Skill 1: Identify, document, review and design complex IT enabled business processes that define a set of activities that will accomplish specific organisational goals and provides a systematic approach to improving those processes (C BCM S1)\r\n•\t\r\nNOTE:\r\nAll learning outcomes are aligned to the Digital and Technology Solutions Specialist (integrated degree) which is subject to change every three years from the date of publication E.g. Next review is August 7 2021. \r\nhttps://www.instituteforapprenticeships.org/apprenticeship-standards/digital-and-technology-solutions-specialist-integrated-degree/FalseNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNThis module will apply the tenets of Project Based Learning (PBL) whereby apprentices are presented with a challenge that requires apprentices to produce a product in response to the challenge.\r\n(For apprentices with less technical background more scaffolding will be available, but the integrity of the challenge will not be reduced. They will become confident in approaching and understanding how to solve the problem.) \r\n\r\nChallenge and lecture materials are released at the start of the week, apprentices have a couple of days to try and solve the challenge with the tools at their disposal. They may use drop in sessions, and group discussion boards to collaboratively solve or discuss ideas. They submit their attempt(s) at the challenge in advance of the live seminar so that demonstrators and instructors can provide feedback. After the seminar there is another lab session to reinforce and walk through the steps again, should apprentices need additional practice and support.There are two summative assessments in this module that include an individual Lab Report and a report. The first is intended to assess individual’s technical understanding of the content. The second report is a description of their contribution to the group work activities that include: software interaction and architecture planning, implementation steps and evaluation as well as consideration of legal, social and ethical perspectives for their solution.\r\n\r\nTo support the summative assessments, there are non-assessed weekly labs. These provide apprentices the opportunity to learn and discuss technical skills, as well as receive step by step guidance from a demonstrator. Attending these sessions will help apprentices practice, master, and write their summative Lab Report.\r\nOther activities that exist to support mastery of learning, but not formatively assessed, include weekly workshops, and skills practice in the workplace. Workshops allow the instructor to meet with groups regularly for a status update on their work towards their final project, help address any technical or group questions and issues, and provide groups feedback. Workshops are also an opportunity to learn from University researchers and practitioners in the workplace in the form of seminars and activities to help build apprentices interpersonal skills. \r\n\r\nThis apprenticeship recognizes that not all learning can be mastered in one day, therefore there is time allocated for practicing skills, meaning apprentices can transfer their knowledge and skill into the workplace. These opportunities need to be identified during tri monthly tripartite meetings with the apprentice, their manager and their teaching fellow. These events are then documented in another platform (APTEM) which members of the tripartite conversation can access.FalseNaNNaNFalseFalseFalseFalseNaNNaN202222D-COMPM00311133728-09-2022FalseFalseFalseFalseNewcastle City Campus
5350CSC843453395875Server-side DevelopmentServer-side Development999TrueFalseTrue10False0False057LBLNTrueTrue03-11-2022True03-11-2022True03-11-2022NaNNaNEThis module aims to equip apprentices with the technical and practical experience to function as successful software engineering practitioners. It will provide apprentices with a Project Based challenge* that requires them to learn and apply their skills to design, implement and run a three-tier business application end-to-end in Java (that is, from client to database), previously learned skills in Web-technologies, Human-Computer-Interaction, Project Management and Software Engineering processes.\r\n\r\nIn this module, the apprentices will design, develop, and manage a software engineering project and its timeline following agile, lean or other industry-recognized approaches. They will be able to transfer the understanding of three-tier socket-based applications to a web-based setting (based on HTTP). They will apply HCI methodologies to design a product driven by user experience that incorporates an extended knowledge of Java. Further, they will create a presentation based on their product for stakeholders.The module will cover:\r\n\r\n•\tAdvanced Programming:\r\no\tServer side programming\r\no\tCommon data structures and manipulation of collections of objects.\r\no\tConcurrent programming\r\n•\tIntroduction to Database SystemsNTo be able to\r\n-(K2) Describe & discuss advanced features of Java programming including: \r\n\t-Concurrent programming,\r\n\t-Server programming\r\n\r\n-(K1, K2) Explain the relevant technology underlying web content delivery and presentation\r\n-(K2, K4) Describe database system design and use\r\n\r\nDegree Apprenticeship Standards\r\nThe outcomes listed above support the Digital and Technology Solutions Specialist; Software Engineering Specialist, Level 7 domain specific outcomes:\r\n•\tKnowledge 1: The various inputs, statements of requirements, security considerations and constraints that guide solution architecture and the development of logical and physical systems' designs;\r\n•\tKnowledge 2: The approaches used to modularise the internal structure of an application and describe the structure and behaviour of applications used in a business, with a focus on how they interact with each other and with business users;\r\n\r\nImportant to note that should MoF outcomes be adjusted they directly impact the credibility of the Apprenticeships Domain standards. The Domain standards are also listed in the End Point Assessment (EPA), Capstone MOF, and need to be introduced early in the programme to help apprentices develop and practice these skills.IIntended Skills Outcomes:\r\nTo be able to\r\n•\tUse advanced features of Java programs including (S3):\r\n•\tIdentify the principal hardware components and software services which provide infrastructure for\r\nthe Internet from the global scale down to the desktop (S3).\r\n•\tConstruct simple web-based applications using common, current tools and systems.(S3) (C PC S7)\r\nIdentify, document, review and design IT enabled business processes that define a set of activities that will accomplish specific organisational goals and provides a systematic approach to improving processes (S6) (C PC S7)\r\nIntended Behaviour Outcomes:\r\nTo be able to\r\n•\tInspire and motivate others to deliver excellent technical solutions and outcomes (C L B1)\r\n•\tEstablish high levels of performance in digital and technology solutions activities (C L B2)\r\n•\tCreate strong positive relationship with team members to produce high performing technical teams (C L B3)\r\n•\tBe results and outcomes driven to achieve high key performance outcomes for digital and technology solutions objectives (C L B4)\r\n•\tPromote a high level of cooperation between own work group and other groups to establish a technology change led culture (C L B5)\r\n•\tDevelop and support others in developing an appropriate balance of leadership and technical skills (C L B6)\r\n\r\nDegree Apprenticeship Standards\r\nThe outcomes listed above support the Digital and Technology Solutions Specialist; Software Engineering Specialist, Level 7 domain specific outcomes:\r\n•\tSkill 3: Develop and deliver, distributed or semi-complex software solutions that are scalable and which deliver innovative user experiences and journeys that encompass cross-functional teams, platforms and technologies\r\n•\tSkill 6: Be accountable for the quality of deliverables from one or more software development teams (source code quality, automated testing, design quality, documentation etc.) and following company standard processes (code reviews, unit testing, source code management etc.).\r\nThis MoF also aligns with the Core standards (C)\r\n•\tLeadership Behaviour 1: Inspire and motivate others to deliver excellent technical solutions and outcomes (C L B1)\r\n•\tLeadership Behaviour 2: Establish high levels of performance in digital and technology solutions activities (C L B2)\r\n•\tLeadership Behaviour 3: Be results and outcomes driven to achieve high key performance outcomes for digital and technology solutions objectives (C L B3)\r\n•\tLeadership Behaviour 4: Promote a high level of cooperation between own work group and other groups to establish a technology change led culture. (C L B4)\r\n•\tLeadership Behaviour 5: Develop and support others in developing an appropriate balance of leadership and technical skills (C L B5)\r\n•\tLeadership Behaviour 6: Create strong positive relationship with team members to produce high performing technical teams (C L B6)\r\n•\tProfessional Competencies Skill 7: Demonstrate self-direction and originality in solving problems, and act autonomously in planning and implementing digital and technology solutions specialist tasks at a professional level (C PC S7)\r\n•\tBusiness and Change Management Skill 1: Identify, document, review and design complex IT enabled business processes that define a set of activities that will accomplish specific organisational goals and provides a systematic approach to improving those processes (C BCM S1)\r\n•\r\nNOTE:\r\nAll learning outcomes are aligned to the Digital and Technology Solutions Specialist (integrated degree) which is subject to change every three years from the date of publication E.g. Next review is August 7 2021.\r\nhttps://www.instituteforapprenticeships.org/apprenticeship-standards/digital-and-technology- solutions-specialist-integrated-degree/FalseNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNThis module will apply the tenets of Project Based Learning (PBL) whereby apprentices are presented with a challenge that requires apprentices to produce a product in response to the challenge.\r\n(For apprentices with less technical background more scaffolding will be available, but the integrity of the challenge will not be reduced. They will become confident in approaching and understanding how to solve the problem.) \r\n\r\nChallenge and lecture materials are released at the start of the week, apprentices have a couple of days to try and solve the challenge with the tools at their disposal. They may use drop in sessions, and group discussion boards to collaboratively solve or discuss ideas. They submit their attempt(s) at the challenge in advance of the live seminar so that demonstrators and instructors can provide feedback. After the seminar there is another lab session to reinforce and walk through the steps again, should apprentices need additional practice and support.There is one summative assessment in this module intended to assess the technical understanding of the content covered. \r\n\r\nTo support the summative assessments, there are non-assessed weekly labs. These provide apprentices the opportunity to learn and discuss technical skills, as well as receive step by step guidance from a demonstrator. Attending these sessions will help apprentices practice, master, and write their summative Lab Report.\r\nOther activities that exist to support mastery of learning, but not formatively assessed, include weekly workshops, and skills practice in the workplace. Workshops allow the instructor to meet with groups regularly for a status update on their work towards their final project, help address any technical or group questions and issues, and provide groups feedback. Workshops are also an opportunity to learn from University researchers and practitioners in the workplace in the form of seminars and activities to help build apprentices interpersonal skills.\r\n\r\nThis apprenticeship recognizes that not all learning can be mastered in one day, therefore there is time allocated for practicing skills, meaning apprentices can transfer their knowledge and skill into the workplace. These opportunities need to be identified during tri monthly tripartite meetings with the apprentice, their manager and their teaching fellow. These events are then documented in another platform (APTEM) which members of the tripartite conversation can access.FalseNaNNaNFalseFalseFalseFalseNaNNaN202222D-COMPM00311133828-09-2022FalseFalseFalseFalseNewcastle City Campus
5351CSC843553396173Web Client ApplicationsWeb Client Applications999FalseFalseFalse0False0True1057LBLNTrueTrue03-11-2022True03-11-2022True03-11-2022NaNNaNEThis module aims to equip apprentices with the technical and practical experience to function as successful software engineering practitioners. It will provide apprentices with a practical challenge that requires them to learn and apply their skills in web application development, demonstrating an understanding of user interface design, and their ability to create meaningful web content.\r\n\r\nThey will also produce reports to demonstrate their understanding of accessibility issues and application testing.The module will cover:\r\n\r\n•\tWeb Technologies:\r\no\tBasic languages (e.g. HTML, CSS, JavaScript).\r\no\tForms & scripts\r\no\tWeb architecture\r\no\tClient-server relationship\r\no\tHTTP and REST protocols\r\no\tWeb standards and validation\r\no\tResponsive web designNTo be able to:\r\n\r\n-Web application development \r\n\r\n•\t(K1)Describe current and emerging Internet technologies\r\n•\t(K1, K2)Explain the relevant technology underlying web content delivery and presentation\r\n\r\n\r\nDegree Apprenticeship Standards\r\nThe outcomes listed above support the Digital and Technology Solutions Specialist; Software Engineering Specialist, Level 7 domain specific outcomes:\r\n•\tKnowledge 1: The rationale for software platform and solution development, including the organisational context.\r\n•\tKnowledge 2: The various inputs, statements of requirements, security considerations and constraints that guide solution architecture and the development of logical and physical systems' designs;\r\n\r\nImportant to note that should MoF outcomes be adjusted they directly impact the credibility of the Apprenticeships Domain standards. The Domain standards are also listed in the End Point Assessment (EPA), Capstone MOF, and need to be introduced early in the programme to help apprentices develop and practice these skills.Intended Skills Outcomes:\r\nTo be able to\r\n\r\n•\tConstruct simple web-based applications using common, current tools and systems.(S3) (C PC S7)\r\n•\tIdentify, document, review and design IT enabled business processes that define a set of activities that will accomplish specific organisational goals and provides a systematic approach to improving processes (S6) (C PC S7)\r\n\r\nIntended Behaviour Outcomes:\r\nTo be able to\r\n•\tInspire and motivate others to deliver excellent technical solutions and outcomes (C L B1)\r\n•\tEstablish high levels of performance in digital and technology solutions activities (C L B2)\r\n•\tCreate strong positive relationship with team members to produce high performing technical teams (C L B3)\r\n•\tBe results and outcomes driven to achieve high key performance outcomes for digital and technology solutions objectives (C L B4)\r\n•\tPromote a high level of cooperation between own work group and other groups to establish a technology change led culture (C L B5)\r\n•\tDevelop and support others in developing an appropriate balance of leadership and technical skills (C L B6)\r\n\r\nDegree Apprenticeship Standards\r\nThe outcomes listed above support the Digital and Technology Solutions Specialist; Software Engineering Specialist, Level 7 domain specific outcomes:\r\n•\tSkill 3: Develop and deliver, distributed or semi-complex software solutions that are scalable and which deliver innovative user experiences and journeys that encompass cross-functional teams, platforms and technologies\r\n•\tSkill 6: Be accountable for the quality of deliverables from one or more software development teams (source code quality, automated testing, design quality, documentation etc.) and following company standard processes (code reviews, unit testing, source code management etc.).\r\n\r\nThis MoF also aligns with the Core standards (C)\r\n•\tLeadership Behaviour 1: Inspire and motivate others to deliver excellent technical solutions and outcomes (C L B1)\r\n•\tLeadership Behaviour 2: Establish high levels of performance in digital and technology solutions activities (C L B2)\r\n•\tLeadership Behaviour 3: Be results and outcomes driven to achieve high key performance outcomes for digital and technology solutions objectives (C L B3)\r\n•\tLeadership Behaviour 4: Promote a high level of cooperation between own work group and other groups to establish a technology change led culture. (C L B4)\r\n•\tLeadership Behaviour 5: Develop and support others in developing an appropriate balance of leadership and technical skills (C L B5)\r\n•\tLeadership Behaviour 6: Create strong positive relationship with team members to produce high performing technical teams (C L B6)\r\n•\tProfessional Competencies Skill 7: Demonstrate self-direction and originality in solving problems, and act autonomously in planning and implementing digital and technology solutions specialist tasks at a professional level (C PC S7)\r\n•\tBusiness and Change Management Skill 1: Identify, document, review and design complex IT enabled business processes that define a set of activities that will accomplish specific organisational goals and provides a systematic approach to improving those processes (C BCM S1)\r\n•\r\nNOTE:\r\nAll learning outcomes are aligned to the Digital and Technology Solutions Specialist (integrated degree) which is subject to change every three years from the date of publication E.g. Next review is August 7 2021.\r\nhttps://www.instituteforapprenticeships.org/apprenticeship-standards/digital-and-technology- solutions-specialist-integrated-degree/FalseNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNThis module will apply the tenets of Project Based Learning (PBL) whereby apprentices are presented with a challenge that requires apprentices to produce a product in response to the challenge.\r\n(For apprentices with less technical background more scaffolding will be available, but the integrity of the challenge will not be reduced. They will become confident in approaching and understanding how to solve the problem.)\r\n\r\nChallenge and lecture materials are released at the start of the week, apprentices have a couple of days to try and solve the challenge with the tools at their disposal. They may use drop in sessions, and group discussion boards to collaboratively solve or discuss ideas. After the seminar there is another lab session to reinforce and walk through the steps again, should apprentices need additional practice and support.There is one summative assessment in this module intended to assess the technical understanding of the content covered. \r\n\r\nTo support the summative assessments, there are non-assessed weekly labs. These provide apprentices the opportunity to learn and discuss technical skills, as well as receive step by step guidance from a demonstrator. Attending these sessions will help apprentices practice, master, and write their summative Lab Report.\r\nOther activities that exist to support mastery of learning, but not formatively assessed, include weekly workshops, and skills practice in the workplace. Workshops allow the instructor to meet with groups regularly for a status update on their work towards their final project, help address any technical or group questions and issues, and provide groups feedback. Workshops are also an opportunity to learn from University researchers and practitioners in the workplace in the form of seminars and activities to help build apprentices interpersonal skills.\r\n\r\nThis apprenticeship recognizes that not all learning can be mastered in one day, therefore there is time allocated for practicing skills, meaning apprentices can transfer their knowledge and skill into the workplace. These opportunities need to be identified during tri monthly tripartite meetings with the apprentice, their manager and their teaching fellow. These events are then documented in another platform (APTEM) which members of the tripartite conversation can access.FalseNaNNaNFalseFalseFalseFalseNaNNaN202222D-COMPM00311134029-09-2022FalseFalseFalseFalseNewcastle City Campus
5352NBS862953432137EconometricsEconometrics999TrueFalseTrue10False0False057LSTNTrueTrue15-11-2022True19-10-2022True15-11-2022NaNNaNEThe course introduces graduate level econometrics. The module provides students with the basic tools for understanding applied research and with solid mathematical foundations of econometric techniques.\r\n\r\nThe module covers the Linear Regression model in depth. Students are going to learn about the ordinary least squares estimator, its properties, and use in statistical inference. Students will learn how to deal with violations of the Gauss-Markov assumptions.1. Introduction. What econometrics is about / The inference problem / Reminder of finite sample OLS properties.\r\n\r\n2. Statistical inference. Reminder on asymptotic theory (Law of Large Numbers, The Central Limit Theorem) / Asymptotics of OLS.\r\n\r\n3. Non-Spherical disturbances. Heteroscedasticity, Clustering, Autocorrelation.\r\n\r\n4. Identification Issues in the Linear Model. Omitted variable bias, Measurement error bias, Functional form misspecification.NAt the end of the module students should be able to:\r\n\r\n1. Understand the theoretical properties of the Ordinary Least Squares estimator.\r\n\r\n2. Understand the potential violations of the Gauss-Markov Assumptions and the identification issues in the Linear Model.\r\n\r\n3. Know the relevant commands to these techniques on real-world data using R.At the end of the module students should be able to:\r\n\r\n1. Conduct their empirical studies applying the covered techniques.\r\n\r\n2. Critically evaluate (their own or published) estimates and identify potential issues that arise when estimating a linear model.\r\n \r\n3. Have practised extensively their problem solving skills and have become experienced users of econometric software.TrueNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNLectures provide an exhaustive and in-depth introduction to the core course material, and introduction to required techniques. Computer classes will teach the application of these methods with real world data. Private study facilitates review and understanding of lecture material.Unseen written in person exam is the best way to assess graduate level econometrics to the required standards. The formative assessment will give students the opportunity to receive feedback on exercises that will help guide their exam preparation.FalseNaNNaNFalseFalseFalseFalseNaNNaN202222D-NUBSM00311134216-11-2022FalseFalseFalseFalseNewcastle City Campus
5353NBS863053416710Mathematics FoundationsMathematics Foundations999TrueFalseFalse0False0False007BSTNTrueTrue15-11-2022True15-11-2022True15-11-2022NaNNaNE• The aim of this blocked course is to prepare students for advanced studies in “Mathematics for\r\nEconomics and Finance”, “Introductory Econometrics”, and economic theory modules.\r\n• This course aims at refreshing univariate and multivariate calculus.\r\n• It also aims at introducing key concepts of linear algebra and illustrates the role matrices\r\nplay in economics, and econometrics.• Real analysis (series and sequences, continuous functions, Taylor’s theorem, mean and extreme value theorems)\r\n• Univariate and multivariate calculus\r\n• Algebra of matrices and linear mappings’, determinants, diagonalization, and canonical forms, and vector spaces\r\n• Probability functions, conditional probability, permutations and combinations, Bayes rule\r\n• Continuous and discrete random variables, univariate and multivariate distributions, expectation and conditional expectation, moments\r\n• Sampling distributions, hypothesis testing, confidence intervalsNStudents will be able to manipulate equations and analyse properties of continuous and differentiable functions using calculus.\r\n\r\nStudents will have a good working knowledge of matrix algebra.\r\n\r\nStudent will understand key concepts of probability theory, random variables, and statistical inference.Student will develop problem solving skills using calculus, real analysis, matrix algebra and statistics.\r\n\r\nStudents will be able to analyse economic problems using a variety of mathematical and statistical techniques and interpret the results.TrueNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN• Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on homework.NaNFalseNaNNaNFalseFalseFalseFalseNaNNaN202222D-NUBSM00911134325-10-2022FalseFalseFalseFalseNewcastle City Campus
5354NBS863153432699DissertationDissertation999TrueFalseFalse0False0True60307LSTNTrueTrue15-11-2022True15-11-2022True15-11-2022NaNNoneETo provide the opportunity for students to undertake an independent research project in Economics in which they apply the academic skills acquired during the year. The research will be supervised by a member of the academic staff, whose research interests will be closely aligned with the topic of research. Students will have supervisory meetings in the final term of the academic year; these meetings are for the MSc supervisor to offer guidance and monitor progress of the student.The dissertation is a major piece of work which will enable students to apply the knowledge and skills developed during the taught element of the degree programme to their chosen research topic. As such there is no set syllabus for the dissertation module.NAfter completing the dissertation, students will: \r\n\r\n1. Understand the nature of general academic research. \r\n\r\n2. Gain in-depth knowledge of a general research topic in economics, both from an empirical and theoretical angle, including issues that are may not be settled in the literature related to such topic. \r\n\r\n3. Be able to propose or explain potential answers to one or more academic questions related to the particular topic chosen for the dissertation.After completing the dissertation you will: \r\n\r\n1. Be able to undertake a literature review by identifying and critically assessing empirical and theoretical contributions to the literature, and presenting the conclusions in a structured way. \r\n\r\n2. Be able to formulate a research question and motivate it in relation to the findings and conclusions derived from the literature review. \r\n\r\n3. Be able to use novel econometric or suitable quantitative methods to obtain answers to a research question related to Economics, describe them and present them in a logical way. \r\n\r\n4. Be able to analyse the findings from the empirical exercise and critically assess them in relation to theoretical arguments and existing literature. \r\n\r\n5. Be able to organize all of the above in a structured way to produce a written report (dissertation).FalseNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNResearch Methods will underpin the dissertation, the dissertation is therefore an independent piece of work that will be monitored through the tutorial system.An in-depth written report allows the student to demonstrate his/ her ability to undertake independent research.FalseNaNNaNFalseFalseFalseFalseNaNNaN202222D-NUBSM00311134416-11-2022FalseFalseFalseFalseNewcastle City Campus
5355CAC207053423099Exploring the Ancient Greek UnderworldExploring the Ancient Greek Underworld999TrueFalseFalse0True20False0105LSTNTrueTrue03-11-2022True03-11-2022True03-11-2022NaNNaNEWhat happens after death? Is there some spirit, soul, or essence of our personalities which survives – and if so, where does it go? And will our actions during life determine our eternal fate? For thousands of years humans have grappled with a myriad of questions about existence after death, and – as in many cultures – the Ancient Greeks had plenty of different ideas. Across the semester, students will be introduced to these Ancient Greek beliefs about death and the afterlife, with a particular focus on the Underworld. Using a variety of textual and material evidence from across the Archaic, Classical, and Hellenistic periods, we will explore the Underworld itself: from the journey there, to topographies and inhabitants, as well as the potential for reincarnation and even resurrection from its shadowy depths.\r\n\r\nAll materials will be studied in translation; there is no expectation or requirement that students have any knowledge of Ancient Greek or Latin.Topics studied during the semester may include:\r\n\r\n+ Death and the Body \r\n+ Navigating the Underworld\r\n+ Underworld Gods\r\n+ Underworld Inhabitants\r\n+ Judgement\r\n+ Reincarnation\r\n+ Heroic katabaseis\r\n+ Necromancy\r\n+ Underworld anxieties\r\n+ Reception and Reinterpretation\r\n\r\nTexts/works studied during the semester may include:\r\n+ Homer Iliad and Odyssey\r\n+ Homeric Hymns\r\n+ Hesiod Theogony\r\n+ Presocratic Philosophy\r\n+ Euripides Alcestis, Heracles\r\n+ Aristophanes Frogs\r\n+ Plato Phaedo, Republic\r\n+ Orphic Gold Tablets\r\n+ Epicurus Letters \r\n+ Apollonius Argonautica\r\n+ Diodorus Siculus Library of History\r\n\r\nPlus a range of material and visual evidence.CStudents who complete this course should acquire: \r\n\r\n1. An understanding of the multifaceted nature of ancient thought and beliefs; \r\n2. An in-depth knowledge of key mythological and iconographical representations of the Underworld; \r\n3. A familiarity with a wide range of texts including epic, tragedy, and philosophy; \r\n4. An awareness of how ideas are received and resituated across cultures and periods.Students who complete this course should acquire:\r\n\r\n1. Skills in close reading and critically evaluating a wide range of evidence-types (in translation); \r\n2. Skills in selecting and reviewing relevant modern secondary literature; \r\n3. An ability to apply learned knowledge and skills in the completion of assessment components; \r\n4. An ability to reflect on, and engage in dialogue with, questions arising from the studied material.TrueNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNLectures are used to introduce students to a wide range of evidence and broader ancient beliefs. The content will be supported by relevant contextual and historical information where necessary. They also model methods of interpretation and analysis, and draw attention to comparative ideas. Elements of group-work and student-teacher interaction will reinforce the delivered material. \r\n\r\nSeminars are used to facilitate student-led discussion on a particular evidence type and pre-circulated questions in a small structured environment. It provides the opportunity for students to explore the material for themselves, drawing on weekly lectures, and to enter into a dialogue with each other on the multifaceted nature of meaning and interpretation. \r\n\r\nWorkshops are used to ensure the assessment aims are clearly articulated and understood by students ahead of their submissions, and provide focused instruction and practice in developing specific skills: structuring an argument, finding relevant bibliographical sources, referencing classical texts, etc. They also allow for student-teacher dialogue on expectations, marking criteria, and feedback.The portfolio is an opportunity for students to accrue credit throughout the semester for their ongoing seminar preparation work; set tasks for the portfolio target skills of close reading, reflection, and analysis.\r\n\r\nThe final project supports and encourages independent research, using lecture and seminar content as a foundation on which to build one’s own avenue of investigation in the development of educational resources. It provides an authentic opportunity to apply skills and knowledge at an appropriate level of detail and understanding, while also allowing engagement with the material over a sustained period of time. It also allows students to practice ‘translating’ complex ideas into accessible formats. Specialist training and guidance on developing educational outreach resources will be provided.\r\nThe formative assignment provides support during the initial planning period for the final project, giving an opportunity for feedback and guidance in the critical stages of preparation.FalseNaNNaNFalseFalseFalseFalseNaNNaN202222D-SHISM00111134804-11-2022FalseFalseFalseFalseNewcastle City Campus
5356CAC307053423150Exploring the Ancient Greek UnderworldExploring the Ancient Greek Underworld999TrueFalseFalse0True20False0105LSTNTrueTrue03-11-2022True03-11-2022True03-11-2022NaNNaNEWhat happens after death? Is there some spirit, soul, or essence of our personalities which survives – and if so, where does it go? And will our actions during life determine our eternal fate? For thousands of years humans have grappled with a myriad of questions about existence after death, and – as in many cultures – the Ancient Greeks had plenty of different ideas. Across the semester, students will be introduced to these Ancient Greek beliefs about death and the afterlife, with a particular focus on the Underworld. Using a variety of textual and material evidence from across the Archaic, Classical, and Hellenistic periods, we will explore the Underworld itself: from the journey there, to topographies and inhabitants, as well as the potential for reincarnation and even resurrection from its shadowy depths.\r\n\r\nAll materials will be studied in translation; there is no expectation or requirement that students have any knowledge of Ancient Greek or Latin.Topics studied during the semester may include:\r\n\r\n+ Death and the Body \r\n+ Navigating the Underworld\r\n+ Underworld Gods\r\n+ Underworld Inhabitants\r\n+ Judgement\r\n+ Reincarnation\r\n+ Heroic katabaseis\r\n+ Necromancy\r\n+ Underworld anxieties\r\n+ Reception and Reinterpretation\r\n\r\nTexts/works studied during the semester may include:\r\n+ Homer Iliad and Odyssey\r\n+ Homeric Hymns\r\n+ Hesiod Theogony\r\n+ Presocratic Philosophy\r\n+ Euripides Alcestis, Heracles\r\n+ Aristophanes Frogs\r\n+ Plato Phaedo, Republic\r\n+ Orphic Gold Tablets\r\n+ Epicurus Letters \r\n+ Apollonius Argonautica\r\n+ Diodorus Siculus Library of History\r\n\r\nPlus a range of material and visual evidence.CStudents who complete this course should acquire: \r\n\r\n1. An understanding of the multifaceted nature of ancient thought and beliefs; \r\n2. An in-depth knowledge of key mythological and iconographical representations of the Underworld; \r\n3. A familiarity with a wide range of texts including epic, tragedy, and philosophy; \r\n4. An awareness of how ideas are received and resituated across cultures and periods.Students who complete this course should acquire:\r\n\r\n1. Skills in close reading and critically evaluating a wide range of evidence-types (in translation); \r\n2. Skills in selecting and reviewing relevant modern secondary literature; \r\n3. An ability to apply learned knowledge and skills in the completion of assessment components; \r\n4. An ability to reflect on, and engage in dialogue with, questions arising from the studied material.TrueNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNLectures are used to introduce students to a wide range of evidence and broader ancient beliefs. The content will be supported by relevant contextual and historical information where necessary. They also model methods of interpretation and analysis, and draw attention to comparative ideas. Elements of group-work and student-teacher interaction will reinforce the delivered material. \r\n\r\nSeminars are used to facilitate student-led discussion on a particular evidence type and pre-circulated questions in a small structured environment. It provides the opportunity for students to explore the material for themselves, drawing on weekly lectures, and to enter into a dialogue with each other on the multifaceted nature of meaning and interpretation. \r\n\r\nWorkshops are used to ensure the assessment aims are clearly articulated and understood by students ahead of their submissions, and provide focused instruction and practice in developing specific skills: structuring an argument, finding relevant bibliographical sources, referencing classical texts, etc. They also allow for student-teacher dialogue on expectations, marking criteria, and feedback.The portfolio is an opportunity for students to accrue credit throughout the semester for their ongoing seminar preparation work; set tasks for the portfolio target skills of close reading, reflection, and analysis.\r\n\r\nThe final project supports and encourages independent research, using lecture and seminar content as a foundation on which to build one’s own avenue of investigation in the development of educational resources. It provides an authentic opportunity to apply skills and knowledge at an appropriate level of detail and understanding, while also allowing engagement with the material over a sustained period of time. It also allows students to practice ‘translating’ complex ideas into accessible formats. Specialist training and guidance on developing educational outreach resources will be provided.\r\nThe formative assignment provides support during the initial planning period for the final project, giving an opportunity for feedback and guidance in the critical stages of preparation.FalseNaNNaNFalseFalseFalseFalseNaNNaN202222D-SHISM00111134904-11-2022FalseFalseFalseFalseNewcastle City Campus
5357EXT804053456273Aerospace ManufacturingAerospace Manufacturing999TrueFalseFalse0True10False057LSTNTrueTrue06-12-2022True06-12-2022True06-12-2022NaNNaNENaNThis module covers:\r\n\r\nBasic airframe structure. Airframe component manufacturing techniques. Joining techniques. Assembly\r\ntechnology. Composite structures. Jigless assembly and automated manufacture.\r\n\r\nBasic aero-engine structure. Geometry and material constraints. Manufacturing processes: forging, casting,\r\nwelding & joining techniques, special processes, small and non round hole manufacture.\r\n\r\nCertification, verification inspection and quality control.NOn successful completion of this module students will be able to:\r\n\r\nUnderstand the basic structure of modern airframes and aero-engines and the manufacturing\r\nprocesses used to produce them.\r\n\r\nHave an insight into advanced and emerging aerospace manufacturing technologies and be able\r\nto critically review the suitability of new emerging technologies for the specific requirements and\r\ncharacteristics of the aerospace industry.On successful completion of this module students will have:\r\n\r\nHave an awareness of the properties and application areas for advanced materials used in the\r\nmanufacture of aerospace components.\r\n\r\nHave the ability to communicate and present state of the art technology review results with clear\r\njustification of their technological and operational advantages and disadvantages.FalseNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNaNNaNFalseNaNNaNFalseFalseFalseFalseNaNNaN202222D-SENGM00111720907-12-2022FalseFalseFalseFalseOff Campus
5358EXT804153456274Introduction to Transport MaterialsIntro to Transport Materials999TrueFalseFalse0True10False057LSTNTrueTrue06-12-2022True06-12-2022True06-12-2022Only available to students studying on the Power Electronics CDT programmeNaNETo provide an understanding and knowledge of key concepts in materials science, with particular reference to the use of materials science in the transport industries.Overview/revision of materials classes and properties, and component failure modes.\r\n\r\nStrengths and weaknesses of:\r\nMetallic alloys,\r\nMoulded polymers,\r\nComposites\r\n\r\nIntroduction to processing-property relationships essential to understanding the interactions between manufacturing route and component performance.\r\n\r\nService conditions and property requirements for materials used in:\r\nAutomotive vehicle shells,\r\nAutomotive engines and transmissions,\r\nAirframes,\r\nLanding gear,\r\nGas turbines\r\n\r\nEffects of service conditions on materials behaviour, e.g.\r\nEffects of temperature on creep, Fatigue and oxidation of turbine blades,\r\nEffects of corrosion on fatigue life\r\n\r\nSelection of materials for weight efficiency etc.\r\n\r\nReliability of materials.\r\n\r\nSurface engineering techniques:\r\nEffects on residual stresses,\r\nEffects on fatigue,\r\nEffects on environmental degradation\r\n\r\nOverview of areas of current research relating to transport materials.COn successful completion of this module students will know how to:\r\n\r\n* demonstrate knowledge and understanding of material usage in transport industries with reference to operating conditions and material properties.On successful completion of this module students will be able to:\r\n\r\n* perform material selection analysis based on analysis of operating conditions, material properties and other relevant factors including legislation.\r\n* explain how and why materials engineering can be used to control material properties.\r\n* extract and use relevant information from external sources.FalseNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNaNNaNFalseNaNNaNFalseFalseFalseFalseNaNNaN202222D-SENGM00111721007-12-2022FalseFalseFalseFalseOff Campus